Publications

2024 (peer-reviewed & in alphabetical order)

1. Rutkowski TM, Komendzinski T, Otake-Matsuura M. Mild Cognitive Impairment Prediction and Cognitive Score Regression in the Elderly using EEG Topological Data Analysis and Machine Learning with Awareness Assessed in Affective Reminiscent Paradigm. Frontiers in Aging Neuroscience;15:1294139.

2. Guger C, Allison B, Rutkowski TM, Korostenskaja M, editors. Brain-Computer Interface Research - A State-of-the-Art Summary 11. SpringerBriefs in Electrical and Computer Engineering (BRIEFSELECTRIC). Springer Cham; 2024.

3. Kasprzak H, Niewinska N, Komendzinski T, Otake-Matsuura M, Rutkowski TM. Olfactory Paradigm for Reactive Brain-Computer Interface: EEG Response Spatial Visualization and Clustering. In: Proceedings of The WCCI 2024 International Joint Conference on Neural Networks (IJCNN). Yokohama, Japan: IEEE; 2024. pp. 1-8. doi: 10.1109/IJCNN60899.2024.10650010 [PDF]

4. Kasprzak H, Niewinska N, Komendzinski T, Otake-Matsuura M, Rutkowski TM. Improving the Classification of Olfactory Brain-Computer Interface Responses by Combining EEG and EBG Signals. In: 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Orlando, USA: IEEE Press; 2024. p. (in press).

5. Kojima S, Shiba R, Morimoto Y, Furukawa K, Kanoh S, Komendzinski T, Otake-Matsuura M, and Rutkowski TM. Spatial Auditory Soundscapes for Developing Digital Neurobiomarkers or Cognitive Interventions in Early-onset Dementia Based on EEG and fNIRS Machine-learning Analysis. In: 46th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Orlando, USA: IEEE Press; 2024. p. 1.

6. Rutkowski TM, Kasprzak H, Niewinska N, Otake-Matsuura M, Komendzinski T. Passive Olfactory Brain-computer Interface Paradigm for Awareness Level Prediction. In: Mueller-Putz GR, Kostoglou K, Oberndorfer ME, Wriessnegger SC, editors. Proceedings of the 9th Graz Brain-Computer Interface Conference 2024. Graz, Austria: Verlag der Technischen Universitaet Graz; 2024. p. 201-6. doi: 10.3217/978-3-99161-014-4-036

2023 (peer-reviewed & in alphabetical order)

1. Dresp-Langley B, Rutkowski TM, Toma S. Editorial: Methods and applications in: Cognition. Frontiers in Psychology. 2023;14.

2. Rutkowski TM, Abe MS, Komendzinski T, Sugimoto H, Narebski S, Otake-Matsuura M. Machine Learning Approach for Early Onset Dementia Neurobiomarker Using EEG Network Topology Features. Frontiers in Human Neuroscience;17:262.

3. Rutkowski TM, Abe MS, Sugimoto H, Otake-Matsuura M. Mild Cognitive Impairment Prediction with Machine Learning and Topological Data Analysis Applied to EEG Time-series in Facial Emotion Oddball Paradigm - Objective Neurobiomarker for Early Onset Dementia Prediction Approach -. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). Sydney, Australia: IEEE Press; 2023. p. (accepted, in press).

4. Kodama T, Rutkowski TM, Komendzinski T, Ito YM, Otake-Matsuura M. Multi-modal EEG/fNIRS/ET Data Collection and Cloud Storage System Prototype for the Elderly Home-based Dementia Monitoring. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). Sydney, Australia: IEEE Press; 2023. p. (accepted, in press).

2022 (peer-reviewed & in alphabetical order)

1. Rutkowski TM, Abe MS, Tokunaga S, Komendzinski T, Otake-Matsuura M. Dementia Digital Neuro-biomarker Study from Theta-band EEG Fluctuation Analysis in Facial and Emotional Identification Short-term Memory Oddball Paradigm. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). Glasgow, UK: IEEE Press; 2022. p. 4129-32.

2. Rutkowski TM, Abe MS, Tokunaga S, Sugimoto H, Komendzinski T, Otake-Matsuura M. Passive BCI Oddball Paradigm for Dementia Digital Neuro-biomarker Elucidation from Attended and Inhibited ERPs Utilizing Information Geometry Classification Approaches. In: 2022 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE Systems, Man and Cybernetics. Prag, Czechia: IEEE Press; 2022. p. 2654-2659

3. Rutkowski TM, Narebski S, Bekier P, Komendzinski T, Sugimoto H, Otake-Matsuura M. Cross-cultural Evaluation of Dementia Passive BCI Neuro-biomarker Candidate. In: Proceedings of the 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS & ISIS). IEEE Press; 2022. p. 1-5.

4. Derezinski K, Tolpa K, Furman L, Rutkowski TM, Duch W. Time-frequency Analysis Combined with Recurrence Quantification for Classification of Onset of Dementia Using Data from the Oddball BCI Paradigm. In: Proceedings of the 2022 Joint 12th International Conference on Soft Computing and Intelligent Systems and 23rd International Symposium on Advanced Intelligent Systems (SCIS & ISIS). IEEE Press; 2022. p. 1-6.

2021 (peer-reviewed & in alphabetical order)

1. Rutkowski TM, Abe MS, Tokunaga S, and Otake-Matsuura M. Dementia prediction in older people through topic-cued spontaneous conversation. medRxiv, 2021. doi: 10.1101/2021.05.18.21257366.

2. Rutkowski TM. Passive BCI Paradigm to Elucidate Early Dementia Onset. In: The 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. Virtual: IEEE Press; 2021. p. 5403.

3. Rutkowski TM, Abe MS, Otake-Matsuura M. Neurotechnology and AI Approach for Early Dementia Onset Biomarker from EEG in Emotional Stimulus Evaluation Task. In: The 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. Virtual: IEEE Press; 2021. p. 6682-6685.

4. Rutkowski TM, Abe MS, Komendzinski T, Otake-Matsuura M. Older Adult Mild Cognitive Impairment Prediction from Multiscale Entropy EEG Patterns in Reminiscent Interior Image Working Memory Paradigm. In: The 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. Virtual: IEEE Press; 2021. p. 6352-6355.

5. Rutkowski TM, Abe MS, Tokunaga S, Komendzin ́ski T, Otake-Matsuura M. Classifying Mild Cognitive Impairment from EEG Patterns for Dementia Onset Prediction. In: Proceedings of 14th International Neural Coding Workshop. University of Washington, Bothell, USA; 2021. p. abstract #20.

2020 (peer-reviewed & in alphabetical order)

1. Rutkowski TM, Abe MS, Koculak M, Otake-Matsuura M. Classifying Mild Cognitive Impairment from Behavioral Responses in Emotional Arousal and Valence Evaluation Task - AI Approach for Early Dementia Biomarker in Aging Societies -. In: 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. Montreal, Canada: IEEE Press; 2020. p. 5537-5543.

2. Rutkowski TM, Abe MS, Otake-Matsuura M. EEG and fNIRS Biomarkers of Dementia Prediction and Monitoring. In: The 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. Montreal, Canada: IEEE Press; 2020. p. (accepted, in press).

3. Rutkowski TM, Abe MS, Otake-Matsuura M. Passive BCI for Dementia Onset Detection and Cognitive Intervention Monitoring. In: International Symposium on Artificial Intelligence and Brain Science 2020. Tokyo, Japan: The University of Tokyo; 2020. p. (accepted, in press).

2019 (peer-reviewed & in alphabetical order)

1. Rutkowski TM, Abe MS, Koculak M, Otake-Matsuura M. Cognitive Assessment Estimation from Behavioral Responses in Emotional Faces Evaluation Task - AI Regression Approach for Dementia Onset Prediction in Aging Societies. In: NeurIPS Joint Workshop on AI for Social Good - Track 1 - Producing Good Outcomes. Vancouver, Canada; 2019. p. 1-4. [arXiv copy]

2. Rutkowski TM, Koculak M, Abe MS, Otake-Matsuura M. Brain Correlates of Task–Load and Dementia Elucidation with Tensor Machine Learning Using Oddball BCI Paradigm. In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP); 2019. p. 8578–8582. [arXiv copy]

3. Rutkowski TM, Zhao Q, Abe MS, Otake-Matsuura M. Passive BCI for Task-load and Dementia Biomarker Elucidation. In: 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. Berlin, Germany: IEEE Press; 2019. p. (accepted, in press).

2018 (peer-reviewed & in alphabetical order)

1. Rutkowski TM, Zhao Q, Abe MS, Otake M. AI Neurotechnology for Aging Societies – Task-load and Dementia EEG Digital Biomarker Development Using Information Geometry Machine Learning Methods. In: AI for Social Good Workshop at the Neural Information Processing Systems (NeurIPS = formerly NIPS) 2018. Montreal, Canada; 2018. p. 1-4. [workshop paper repository link]

2. Rutkowski TM. From Active Multisensory Brain-computer Interface Applications for Robotics and Artistic AR/VR Environments to Dementia Monitoring in a Passive Mode - A Human Intelligence Augmentation Approach. In: Proceedings of the 8th International Symposium for Sustainability by Engineering at Mie University (IS2EMU2018-C). Mie University, Japan. Mie University Press; 2018. p. 1–2. [J-news]

3. Rutkowski TM. Multisensory BCIs in applications for robotics, VR/AR, art and dementia monitoring. In: Workshop on Brain-Machine Interface Systems 2018. IEEE International Conference on Systems, Man and Cybernetics. IEEE Systems, Man and Cybernetics. Miyazaki, Japan: IEEE Press; 2018. p. 18–19.

2017 (peer-reviewed & in alphabetical order)

1. Aminaka D, Rutkowski TM. A Sixteen-Command and 40 Hz Carrier Frequency Code-Modulated Visual Evoked Potential BCI. In Brain-Computer Interface Research 2017 (pp. 97-104). Springer, Cham. [link]

2016 (peer-reviewed & in alphabetical order)

1. Aminaka D, Shimizu K, Rutkowski TM. Multiuser Spatial cVEP BCI Direct Brain-robot Control. In: Proceedings of the Sixth International Brain-Computer Interface Meeting: BCI Past, Present, and Future. Asilomar Conference Center, Pacific Grove, CA USA: Verlag der Technischen Universitaet Graz; 2016. p. 70. [link]

2. Chang M, Rutkowski TM. Two-Step Input Spatial Auditory BCI for Japanese Kana Characters. In: Wang R, Pan X, editors. Advances in Cognitive Neurodynamics (V): Proceedings of the Fifth International Conference on Cognitive Neurodynamics - 2015. Singapore: Springer Singapore; 2016. p. 383–389. Available from: [link, arXiv:1503.02903].

3. Higashi H, Rutkowski T, Tanaka T, Tanaka Y. Multilinear Discriminant Analysis With Subspace Constraints for Single-Trial Classification of Event-Related Potentials. IEEE Journal of Selected Topics in Signal Processing. 2016 Oct;10(7):1295–1305. [link]

4. Higashi H, Rutkowski TM, Tanaka T, Tanaka Y. Smoothing of xDAWN Spatial Filters for Robust Extraction of Event–related Potentials. In: Asia-Pacific Signal and Information Processing Association, 2016 Annual Summit and Conference (APSIPA ASC 2016). APSIPA. Jeju, Korea: IEEE Press; 2016. p. paper ID: 193.

5. Kodama T, Shimizu K, Rutkowski TM. Full Body Spatial Tactile BCI for Direct Brain-robot Control. In: Proceedings of the Sixth International Brain-Computer Interface Meeting: BCI Past, Present, and Future. Asilomar Conference Center, Pacific Grove, CA USA: Verlag der Technischen Universitaet Graz; 2016. p. 68. [link]

6. Kodama T, Shimizu K, Makino S, Rutkowski TM. Full–body Tactile P300–based Brain–computer In- terface Accuracy Refinement. In: BioSMART 2016 – Proceedings of International Conference on Bio- engineering for Smart Technologies. Dubai, UAE: American University in Dubai (AUD); 2016. p. 20–23.

7. Kodama T, Makino S, Rutkowski TM. Tactile Brain-computer Interface Using Classification of P300 Responses Evoked by Full Body Spatial Vibrotactile Stimuli. In: Asia-Pacific Signal and Information Processing Association, 2016 Annual Summit and Conference (APSIPA ASC 2016). APSIPA. Jeju, Korea: IEEE Press; 2016. p. paper ID: 176.

8. Pereira Junior J, Teixeira C, Rutkowski TM. Visual Motion Onset Brain–computer Interface. Preprints 2016, pp. 2016090126 (doi: 10.20944/preprints201609.0126.v1); 2016. Available from: [link].

9. Rutkowski TM. Automatic Sleep Staging and Apnea Events Classification from EEG and Multimodal Physiological Signals – Synchrosqueezing Transform Processing and Riemannian Geometry Classification Approaches. In: The 4th Annual IIIS Symposium – Poster Session Abstracts. Tsukuba, Japan: University of Tsukuba; 2016. p. 6. Available from: [link].

10. Rutkowski TM. Data–Driven Multimodal Sleep Apnea Events Detection. Journal of Medical Systems. 2016;40(7):1–7. Available from: link. DOI: 10.1007/s10916-016-0520-7

11. Rutkowski T. Robotic and Virtual Reality BCIs Using Spatial Tactile and Auditory Oddball Paradigms. Frontiers in Neurorobotics. 2016;10:20. Available from: [link].

12. Rutkowski TM. New Developments in AI–based Automatic Sleep Staging and Apnea Events Detection Using Information Geometry and Deep Learning. In: The 5th Annual IIIS Symposium and The 32nd Wako Workshop Joint Meeting – Poster Session Abstracts. Tokyo, Japan: University of Tsukuba; 2016. p. 5.

13. Shimizu K, Kodama T, Makino S, Rutkowski TM. Visual Motion Onset Virtual Reality Brain–computer Interface. In: BioSMART 2016 – Proceedings of International Conference on Bio-engineering for Smart Technologies. Dubai, UAE: American University in Dubai (AUD); 2016. p. 24–27.

14. Stewart T, Hoshino K, Cichocki A, Rutkowski TM. In: Hirose A, Ozawa S, Doya K, Ikeda K, Lee M, Liu D, editors. Motor Priming as a Brain-Computer Interface. vol. 9948 of Lecture Notes in Computer Science. Springer International Publishing; 2016. p. 538–545. Available from: [link].

2015 (peer-reviewed & in alphabetical order)

1. Aminaka D, Makino S, Rutkowski TM. SVM Classification Study of Code–modulated Visual Evoked Potentials. In: Asia-Pacific Signal and Information Processing Association, 2015 Annual Summit and Conference (APSIPA ASC 2015). APSIPA. Hong Kong, China: IEEE Press; 2015. p. 1065–1070.

2. Aminaka D, Makino S, Rutkowski TM. EEG Filtering Optimization for Code-modulated Chromatic Visual Evoked Potential-based Brain-computer Interface. In: Symbiotic Interaction. vol. 9359 of Lecture Notes in Computer Science. Switzerland: Springer International Publishing; 2015. p. 1–6. Available from: [link].

3. Aminaka D, Makino S, Rutkowski TM. Classification Accuracy Improvement of Chromatic and High–Frequency Code–Modulated Visual Evoked Potential–Based BCI. In: Guo Y, Friston K, Aldo F, Hill S, Peng H, editors. Brain Informatics and Health. vol. 9250 of Lecture Notes in Computer Science. Springer International Publishing; 2015. p. 232–241. Available from: [link]. DOI: 10.1007/978-3-319-23344-4_23. The Best Paper Prize awarded at 2015 International Conference Brain Informatics & Health.

4. Aminaka D, Makino S, Rutkowski TM. Chromatic and High–frequency cVEP–based BCI Paradigm. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. IEEE Press; 2015. p. 1906-1909. Available from: [link].

5. Aminaka D, Makino S, Rutkowski TM. SSVEP Brain-computer Interface using Green and Blue Lights. In: Proceedings of The 10th AEARU Workshop on Computer Science and Web Technologies (CSWT- 2015). University of Tsukuba; 2015. p. 39–40.

6. Belhouari A, Berrached N, Rutkowski TM. Classification Improvement and Analysis of P300 Responses with Various Inter–stimulus Intervals in Application to Spatial Visual Brain—computer Interface. In: Asia-Pacific Signal and Information Processing Association, 2015 Annual Summit and Conference (APSIPA ASC 2015). APSIPA. Hong Kong, China: IEEE Press; 2015. p. 1054–1058.

7. Cai Z, Makino S, Rutkowski TM. Brain Evoked Potential Latencies Optimization for Spatial Auditory Brain–Computer Interface. Cognitive Computation. 2015;7(1):34–43. Available from: [link].

8. Chang M, Nakaizumi C, Mori K, Makino S, Rutkowski TM. Spatial Auditory BCI Spellers Using Real and Virtual Surround Sound Systems. In: 6th Conference on Systems Neuroscience and Rehabilitation 2015 (SNR 2015). Tokorozawa, Japan; 2015. p. 28.

9. Hamada K, Mori H, Shinoda H, Rutkowski TM. Airborne Ultrasonic Tactile Display BCI. In: Guger C, Mueller-Putz G, Allison B, editors. Brain-Computer Interface Research - A State-of-the-Art Summary 4. SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing; 2015. p. 57–65. Available from: [link, arXiv].

10. Higashi H, Rutkowski TM, Tanaka T, Tanaka Y. Subspace-Constrained Multilinear Discriminant Analysis for ERP-based Brain Computer Interface Classification. In: Asia-Pacific Signal and Information Processing Association, 2015 Annual Summit and Conference (APSIPA ASC 2015). APSIPA. Hong Kong, China: IEEE Press; 2015. p. 934–940.

11. Kodama T, Makino S, Rutkowski TM. Spatial Tactile Brain-Computer Interface by Applying Vibration to User’s Shoulders and Waist. In: Proceedings of The 10th AEARU Workshop on Computer Science and Web Technologies (CSWT-2015). University of Tsukuba; 2015. p. 41–42.

12. Kono S, Rutkowski TM. Tactile-force brain-computer interface paradigm. Multimedia Tools and Applications. 2015;74(19):8655–8667. Available from: [link].

13. Nakaizumi C, Matsui T, Mori K, Makino S, Rutkowski TM. Spatial Auditory Brain-computer Interface using Head Related Impulse Response. In: Proceedings of The 10th AEARU Workshop on Computer Science and Web Technologies (CSWT-2015). University of Tsukuba; 2015. p. 37–38. arXiv:1501.04374

14. Nakaizumi C, Makino S, Rutkowski TM. Head–related Impulse Response Cues for Spatial Auditory Brain–computer Interface. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. IEEE Press; 2015. p. 1071-1074. Available from: [link]

15. Nakaizumi C, Makino S, Rutkowski TM. Variable Sound Elevation Features for Head–related Impulse Response Spatial Auditory BCI. In: Asia-Pacific Signal and Information Processing Association, 2015 Annual Summit and Conference (APSIPA ASC 2015). APSIPA. Hong Kong, China: IEEE Press; 2015. p. 1094–1099.

16. Rutkowski TM. Brain-Robot and Speller Interfaces Using Spatial Multisensory Brain-computer Interface Paradigms. Frontiers in Computational Neuroscience - Conference Abstract. 2015;(14). Available from: [link].

17. Rutkowski TM. Sleep Monitoring - Multivariate and Multimodal Brain and Peripheral Body Signal Processing Methods for Sleep and Consciousness Level Assessment. In: Abstract Book of the Third APSIPA Workshop on the Frontier in Biomedical Signal Processing and Systems (APSIPA BioSiPS 2015). APSIPA; 2015. p. 5–6.

18. Rutkowski TM. Biomedical signal processing and systems’ state of the arts and future research challenges. In: Asia-Pacific Signal and Information Processing Association, 2015 Annual Summit and Conference (APSIPA ASC 2015). APSIPA. Hong Kong, China: IEEE Press; 2015. p. 1246.

19. Rutkowski TM, Shimizu K, Kodama T, Jurica P, Cichocki A. Brain–robot Interfaces Using Spatial Tactile and Visual BCI Paradigms - Symbiotic Brain–robot Applications. In: Symbiotic Interaction. vol. 9359 of Lecture Notes in Computer Science. Switzerland: Springer International Publishing; 2015. p. 132—137. Available from: [link].

20. Rutkowski TM, Mori H. Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users. Journal of Neuroscience Methods. 2015;244(0):45–51. Brain Computer Interfaces; Tribute to Greg A. Gerhardt. Available from: [link]. DOI 10.1016/j.jneumeth.2014.04.010

21. Rutkowski TM, Mori H, Kodama T, Shinoda H. Airborne Ultrasonic Tactile Display Brain-computer Interface - A Small Robotic Arm Online Control Study. In: Proceedings of The 10th AEARU Workshop on Computer Science and Web Technologies (CSWT-2015). University of Tsukuba; 2015. p. 7–8. arXiv:1502.07762

22. Rutkowski TM, Shinoda H. Airborne ultrasonic tactile display contactless brain-computer interface paradigm. Frontiers in Human Neuroscience. 2015;(16):3-1. Available from: [link].

23. Rutkowski TM, Shimizu K, Kodama T, Jurica P, Cichocki A, Shinoda H. Controlling a Robot with Tactile Brain-computer Interfaces. In: Abstracts of the 38th Annual Meeting of the Japan Neuroscience Society - Neuroscience 2015. BMI/BCI. Kobe, Japan: Japan Neuroscience Society; 2015. p. 2P332. Available from: [link].

24. Rutkowski TM. Student Teaching and Research Laboratory Focusing on Brain–computer Interface Paradigms – A Creative Environment for Computer Science Students –. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. IEEE Press; 2015. p. 3667-3670. Available from: [link]

25. Shimizu K, Aminaka D, Kodama T, Nakaizumi C, Jurica P, Cichocki A, et al. Brain-robot Interfaces Using Spatial Tactile and Visual BCI Paradigms - Brains Connecting to the Internet of Things Approach. In: The International Conference on Brain Informatics & Health - Type II Paper: Proceedings 2015. London, UK: Imperial College London; 2015. p. 9–10.

26. Shimizu K, Makino S, Rutkowski TM. Tactile Pin-pressure Brain-computer Interface. In: Proceedings of The 10th AEARU Workshop on Computer Science and Web Technologies (CSWT-2015). University of Tsukuba; 2015. p. 35–36.

27. Shimizu K, Makino S, Rutkowski TM. Inter–stimulus Interval Study for the Tactile Point–pressure Brain–computer Interface. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. IEEE Press; 2015. p. 1910-1913. Available from: [link]

28. Shimizu K, Kodama T, Jurica P, Cichocki A, Rutkowski TM. Tactile BCI Paradigms for Robots’ Control. In: 6th Conference on Systems Neuroscience and Rehabilitation 2015 (SNR 2015). Tokorozawa, Japan; 2015. p. 28.

29. Yajima H, Makino S, Rutkowski TM. Multi-command Tactile Brain-computer Interface Using the Touch-sense Glove. In: Proceedings of The 10th AEARU Workshop on Computer Science and Web Technologies (CSWT-2015). University of Tsukuba; 2015. p. 43–44.

30. Yajima H, Makino S, Rutkowski TM. Fingertip Stimulus Cue–based Tactile Brain–computer Interface. In: Asia-Pacific Signal and Information Processing Association, 2015 Annual Summit and Conference (APSIPA ASC 2015). APSIPA. Hong Kong, China: IEEE Press; 2015. p. 1059–1064.

2014 (peer-reviewed & in alphabetical order)

1. Alexander T, Kuh A, Hamada K, Mori H, Shinoda H, Rutkowski T. Parallel memory-efficient processing of BCI data. In: Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA); 2014. p. 1–9. Available from: [link], [open access].

2. Aminaka D, Makino S, Rutkowski TM. Chromatic SSVEP BCI paradigm targeting the higher frequency EEG responses. In: Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA); 2014. p. 1–7. Available from: [link], [open access].

3. Chang M, Mori K, Makino S, Rutkowski TM. Spatial Auditory Two-step Input Japanese Syllabary Brain-computer Interface Speller. Procedia Technology. 2014;18:25 – 31. Available from: [link]. DOI 10.1016/j.protcy.2014.11.007

4. Cooper EW, Rutkowski TM, Wierzbicki A, Kobzev GA, Kryssanov VV. Preface. Procedia Technology. 2014;18:1. Available from: [link]. DOI 10.1016/j.protcy.2014.11.002

5. Hamada K, Mori H, Shinoda H, Rutkowski TM. Airborne Ultrasonic Tactile Display Brain-computer Interface Paradigm. In: Mueller-Putz G, Bauernfeind G, Brunner C, Steyrl D, Wriessnegger S, Scherer R, editors. Proceedings of the 6th International Brain-Computer Interface Conference 2014. Graz University of Technology Publishing House; 2014. p. Article ID 018–1–4. Available from [link] arXiv:1404.4184

6. Hieronymus B, Mori H, Rutkowski TM. Brain-computer interface using ambisonics-reproduced dynamic sound image effects. In: Soft Computing and Intelligent Systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), 15th International Symposium on; 2014. p. 301–306. Available from: link.

7. Huggins JE, Guger C, Allison B, Anderson CW, Batista A, Brouwer AM, et al. Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future. Brain-Computer Interfaces. 2014;1(1):27-49. Available from: [link]

8. Kaniwa T, Terasawa H, Matsubara M, Makino S, Rutkowski TM. Electroencephalogram Steady State Response Sonification Focused on the Spatial and Temporal Properties. In: The 20th International Conference on Auditory Display (ICAD-2014). New York, USA; 2014. Available from: [link].

9. Kodama T, Makino S, Rutkowski TM. Spatial Tactile Brain-Computer Interface Paradigm Applying Vibration Stimuli to Large Areas of User’s Back. In: Mueller-Putz G, Bauernfeind G, Brunner C, Steyrl D, Wriessnegger S, Scherer R, editors. Proceedings of the 6th International Brain-Computer Interface Conference 2014. Graz University of Technology Publishing House; 2014. p. Article ID 032–1–4. Available from: [link] arXiv:1404.4226

10. Marin Neto W, Shimizu K, Mori H, Rutkowski TM. Virtual reality feedback environment for brain computer interface paradigm using tactile and bone-conduction auditory modality paradigms. In: Soft Computing and Intelligent Systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), 15th International Symposium on; 2014. p. 469–472. Available from: [link].

11. Mori K, Matsumoto Y, Rutkowski TM. Thresholding the discriminant output improves the reliability of communication using visual P300 speller brain-computer interface (BCI) for physically disabled persons. In: 2014 Neuroscience Meeting Planner. Neuroprosthetics. Washington DC, USA: Society for Neuroscience; 2014. p. Program No. 252.20/KK23. Online. Available from: [link].

12. Mori H, Makino S, Rutkowski TM. Tactile and bone-conduction auditory brain computer interface for vision and hearing impaired users - Stimulus pattern and BCI accuracy improvement. In: Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA); 2014. p. 1–7. Available from: [link], [open access].

13. Nakaizumi C, Matsui T, Mori K, Makino S, Rutkowski TM. Head–related Impulse Response-based Spatial Auditory Brain–computer Interface. In: Mueller-Putz G, Bauernfeind G, Brunner C, Steyrl D, Wriessnegger S, Scherer R, editors. Proceedings of the 6th International Brain-Computer Interface Conference 2014. Graz University of Technology Publishing House; 2014. p. Article ID 020–1–4. Available from: [link] arXiv:1404.3958

14. Rutkowski TM, Hamada K, Mori H, Shinoda H. Novel somatosensory contact-less brain-computer interface paradigm based on airborne ultrasonic tactile display stimuli. In: 2014 Neuroscience Meeting Planner. Neural Decoding. Washington DC, USA: Society for Neuroscience; 2014. p. Program No. 165.11/II20 & DP4. Online. Available from: [link].

15. Rutkowski TM, Mori H, Mori K. Multi-command Tactile and Bone-Conduction-Auditory Brain-Computer Interface. In: Guger C, Vaughan T, Allison B, editors. Brain-Computer Interface Research. SpringerBriefs in Electrical and Computer Engineering. Springer International Publishing; 2014. p. 125-131. Available from: [link].

16. Rutkowski TM, Nakano Y, Shimizu D, Struzik Z, Okada T. Brain Correlates of Creativity - ERP and Time-frequency Creative Insight Responses Analysis. In: Abstracts of the 37th Annual Meeting of the Japan Neuroscience Society - Neuroscience 2014. Yokohama, Japan: Japan Neuroscience Society; 2014. p. O2–H–5–4. Available from: [link].

17. Rutkowski TM. Multichannel EEG sonification with ambisonics spatial sound environment. In: Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA); 2014. p. 1–4. Available from: [link], [open access].

18. Shimizu K, Mori H, Makino S, Rutkowski TM. Tactile pressure brain-computer interface using point matrix pattern paradigm. In: Soft Computing and Intelligent Systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), 15th International Symposium on; 2014. p. 473–477. Available from: [link].

19. Togashi R, Washizawa Y, Kono S, Rutkowski TM. Bayesian delay time estimation of brainwaves using N100 response in tactile-force brain-computer interface. In: Soft Computing and Intelligent Systems (SCIS), 2014 Joint 7th International Conference on and Advanced Intelligent Systems (ISIS), 15th International Symposium on; 2014. p. 292–296. Available from: [link].

20. Yajima H, Makino S, Rutkowski TM. P300 responses classification improvement in tactile BCI with touch-sense glove. In: Asia-Pacific Signal and Information Processing Association, 2014 Annual Summit and Conference (APSIPA). IEEE; 2014. p. 1–7. Available from: [link], [open access].

2013 (peer-reviewed & in alphabetical order)

1. Aminaka D, Mori K, Matsui T, Makino S, Rutkowski TM. Bone-conduction-based Brain Computer Interface Paradigm - EEG Signal Processing, Feature Extraction and Classification. In: Proceedings of the 9th International Conference on Signal Image Technology and Internet Based Systems. Kyoto, Japan: IEEE Computer Society; 2013. p. 818–824.

2. Cai Z, Makino S, Rutkowski TM. Spatial auditory BCI with ERP responses to front-back to the head stimuli distinction support. In: Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific; 2013. p. 1–8. Paper ID 85. Available from: [link].

3. Chang M, Nishikawa N, Struzik ZR, Mori K, Makino S, Mandic D, et al. Comparison of P300 Responses in Auditory, Visual and Audiovisual Spatial Speller BCI Paradigms. In: Proceedings of the Fifth International Brain-Computer Interface Meeting 2013. Asilomar Conference Center, Pacific Grove, CA USA: Graz University of Technology Publishing House, Austria; 2013. p. Article ID: 156. Available from: [link].

4. Chang M, Makino S, Rutkowski TM. Classification improvement of P300 response based auditory spatial speller brain-computer interface paradigm. In: TENCON 2013 - 2013 IEEE Region 10 Conference (31194); 2013. p. 1–4. Paper ID 111. Available from: [link].

5. Hamano T, Rutkowski TM, Terasawa H, Okanoya K, Furukawa K. Generating an Integrated Musical Expression with a Brain-Computer Interface. In: Proceedings of the 13th International Conference on New Interfaces for Musical Expression (NIME 2013). Daejeon + Seoul, Korea Republic: KAIST; 2013. p. 49–54. Available from: [link].

6. Kono S, Aminaka D, Makino S, Rutkowski TM. EEG Signal Processing and Classification for the Novel Tactile-Force Brain-Computer Interface Paradigm. In: Proceedings of the 9th International Conference on Signal Image Technology and Internet Based Systems. Kyoto, Japan: IEEE Computer Society; 2013. p. 812–817. Available from: [link].

7. Lelievre Y, Washizawa Y, Rutkowski TM. Single trial BCI classification accuracy improvement for the novel virtual sound movement-based spatial auditory paradigm. In: Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific; 2013. p. 1–6. Paper ID 365. Available from: [link].

8. Lelievre Y, Rutkowski TM. Novel virtual moving sound-based spatial auditory brain-computer interface paradigm. In: Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on. IEEE Engineering in Medicine and Biology Society; 2013. p. 9–12. ArXiv:1308.2630 [link]. Available from: [link].

9. Matsumoto Y, Makino S, Mori K, Rutkowski TM. Classifying P300 responses to vowel stimuli for auditory brain-computer interface. In: Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific; 2013. p. 1–5. Paper ID 388. Available from: [link].

10. Molla MKI, Tanaka T, Rutkowski TM, Tanaka K. Phase synchronization analysis of EEG channels using bivariate empirical mode decomposition. In: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 2013. p. 1182–1186. Available from: [link].

11. Mori H, Matsumoto Y, Struzik ZR, Mori K, Makino S, Mandic D, et al. Multi-command Tactile and Auditory Brain Computer Interface based on Head Position Stimulation. In: Proceedings of the Fifth International Brain-Computer Interface Meeting 2013. Asilomar Conference Center, Pacific Grove, CA USA: Graz University of Technology Publishing House, Austria; 2013. p. Article ID: 095. Available from: [link].

12. Mori H, Makino S, Rutkowski TM. Multi-command Chest Tactile Brain Computer Interface for Small Vehicle Robot Navigation. In: Imamura K, Usui S, Shirao T, Kasamatsu T, Schwabe L, Zhong N, editors. Brain and Health Informatics. vol. 8211 of Lecture Notes in Computer Science. Springer International Publishing; 2013. p. 469–478. Available from: [link].

13. Mori H, Matsumoto Y, Makino S, Struzik ZR, Mandic DP, Rutkowski TM. Network Based Complexity Analysis in Tactile Brain Computer Interface Task. Transactions of Japanese Society for Medical and Biological Engineering. 2013;51(Supplement):M–134. Available from: [link].

14. Mori H, Matsumoto Y, Kryssanov V, Cooper E, Ogawa H, Makino S, et al. Multi-command Tactile Brain Computer Interface: A Feasibility Study. In: Oakley I, Brewster S, editors. Haptic and Audio Interaction Design 2013 (HAID 2013). vol. 7989 of Lecture Notes in Computer Science. Springer Verlag Berlin Heidelberg; 2013. p. 50–59. Available from: [link].

15. Nakaizumi C, Mori K, Matsui T, Makino S, Rutkowski TM. Auditory Brain-Computer Interface Paradigm with Head Related Impulse Response-based Spatial Cues. In: Proceedings of the 9th International Conference on Signal Image Technology and Internet Based Systems. Kyoto, Japan: IEEE Computer Society; 2013. p. 806–811. Available from: [link].

16. Nishikawa N, Makino S, Rutkowski TM. Spatial auditory BCI paradigm based on real and virtual sound image generation. In: Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific; 2013. p. 1–5. Paper ID 387. Available from: [link].

17. Rutkowski TM, Mori H, Makino S, Mori K. Novel spatial tactile and bone-conduction auditory brain computer interface. In: 2013 Neuroscience Meeting Planner. Brain-Machine Interface I. San Diego, CA, USA: Society for Neuroscience; 2013. p. Program No. 79.03. Online. Available from: [link].

18. Rutkowski TM, Mori H, Matsumoto Y, Struzik ZR, Makino S, Mandic D, et al. Spatial Tactile and Auditory Brain Computer Interface based on Head Position Stimulation. In: Abstracts of the 36th Annual Meeting of the Japan Neuroscience Society - Neuro 2013. Kyoto, Japan: Japan Neuroscience Society; 2013. p. O2–6–3–3. Available from: [link].

19. Rutkowski TM. Beyond Visual P300 Based Brain-Computer Interfacing Paradigms. In: Cooper E, Kobzev GA, Uvarov AF, Kryssanov VV, editors. Proceedings of the Third Postgraduate Consortium International Workshop on Innovations in Information and Communication Science and Technology. Tomsk State University of Control Systems and Radioelectronics. Tomsk, Russia: Publishing House of TUSUR; 2013. p. 277–283. ISBN 978-5-86889-7. Available from: [link].

20. Rutkowski TM, Struzik ZR, Mandic DP. EEG Epileptic Seizures Separation with Multivariate Empirical Mode Decomposition for Diagnostic Purposes. In: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE Engineering in Medicine and Biology Society. Osaka, Japan: IEEE Press; 2013. p. 7128–7131. Available from: [link].

2012 (peer-reviewed & in alphabetical order)

1. Ahmed MU, Rehman N, Looney D, Rutkowski TM, Mandic DP. Dynamical complexity of human responses: a multivariate data-adaptive framework. Bulletin of the Polish Academy of Sciences - Technical Sciences. 2012;60(3):433–445. Available from: [link].

2. Ahmed MU, Rehman N, Looney D, Rutkowski TM, Kidmose P, Mandic DP.Multivariate entropy analysis with data-driven scales. In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on; 2012. p. 3901–3904. Available from: [link].

3. Cai Z, Makino S, Yamada T, Rutkowski TM. Spatial auditory BCI paradigm utilizing N200 and P300 responses. In: Signal Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012 Asia-Pacific; 2012. p. 1–7. Paper ID 355. Available from: [link].

4. Cai Z, Makino S, Rutkowski TM. Spatial Auditory BCI/BMI Paradigm. In: Bandyopadhyay A, editor. Proceedings of The Second International Workshop on Brain Inspired Computing (BIC2012). Tsukuba, Japan: NIMS; 2012. p. poster #2.1.

5. Chang M, Nishikawa N, Cai Z, Makino S, Rutkowski TM. Psychophysical Responses Comparison in Spatial Auditory, Visual, and Audiovisual BCI-Spelling Paradigms. In: Proceedings of The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligent Systems. Kobe, Japan; 2012. p. 2154–2157. Available from: [link].

6. Gallego-Jutgla E, Rutkowski TM, Cichocki A, Sol ́e-Casals J. EEG Signal Analysis via a Cleaning Procedure Based on Multivariate Empirical Mode Decomposition. In: IJCCI 2012 - International Joint Conference on Computational Intelligence, Proceedings. Barcelona, Spain; 2012. p. 670–676.

7. Hori G, Rutkowski TM. Brain Listening – A Sound Installation with EEG Sonification. Journal of the Japanese Society for Sonic Arts. 2012;1(1):17–21.

8. Kaniwa T, Terasawa H, Matsubara M, Rutkowski TM, Makino S. EEG steady state synchrony patterns sonification. In: Signal Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012 Asia-Pacific; 2012. p. 1–6. Paper ID 364. Available from: [link].

9. Matsumoto Y, Nishikawa N, Makino S, Yamada T, Rutkowski TM. Auditory steady-state response stimuli based BCI application - the optimization of the stimuli types and lengths. In: Signal Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012 Asia-Pacific; 2012. p. 1–7. Paper ID 285. Available from: [link].

10. Matsumoto Y, Makino S, Rutkowski TM. Steady-State Auditory Responses Application to BCI/BMI. In: Bandyopadhyay A, editor. Proceedings of The Second International Workshop on Brain Inspired Computing (BIC2012). Tsukuba, Japan: NIMS; 2012. p. poster #2.2.

11. Molla MKI, Islam MR, Tanaka T, Rutkowski TM. Artifact suppression from EEG signals using data adaptive time domain filtering. Neurocomputing. 2012; 97:297–308. Available from: [link].

12. Molla MKI, Tanaka T, Rutkowski TM. Multivariate EMD based approach to EOG artifacts separation from EEG. In: Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on; 2012. p. 653–656. Available from: [link].

13. Mori H, Matsumoto Y, Makino S, Kryssanov V, Rutkowski TM. Vibrotactile Stimulus Frequency Optimization for the Haptic BCI Prototype. In: Proceedings of The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligent Systems. Kobe, Japan; 2012. p. 2150–2153. Available from: [link].

14. Nishikawa N, Matsumoto Y, Makino S, Rutkowski TM. The spatial real and virtual sound stimuli optimization for the auditory BCI. In: Signal Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012 Asia-Pacific; 2012. p. 1–9. Paper ID 356. Available from: [link].

15. Nishikawa N, Makino S, Rutkowski TM. Analysis of Brain Responses to Spatial Real and Virtual Sounds - A BCI/BMI Approach. In: Bandyopadhyay A, editor. Proceedings of The Second International Workshop on Brain Inspired Computing (BIC2012). Tsukuba, Japan: NIMS; 2012. p. poster #2.3 (The Best Poster Award).

16. Ohmura H, Hamano T, Rutkowski TM, Terasawa H, Okanoya K, Furukawa K. Structural Analysis of Responses to Musical Tonality: Commonality with the Neural Processing of Emotion in Language. In: Scott-Philips TC, Tamariz M, Cartmil EA, Hurford JR, editors. Evolution of Language - Proceedings of the 9th International Conference (EVOLANG9). World Scientific; 2012. p. 510–511.

17. Rutkowski TM, Cai Z, Nishikawa N, Matsumoto Y, Makino S, Looney D, et al. A multi-command spatial auditory BMI based on evoked EEG responses from real and virtual sound stimuli. In: 2012 Neuroscience Meeting Planner. Brain Machine Interfaces: Non-Invasive Methods. New Orleans, LA, USA: Society for Neuroscience; 2012. p. Program No. 891.16. Online. Available from: [link].

18. Rutkowski TM, Mori H, Matsumoto Y, Cai Z, Chang M, Nishikawa N, et al. Haptic BCI Paradigm based on Somatosensory Evoked Potential. ArXiv e-prints. 2012 Jul; Available from: [link].

19. Rutkowski TM. Analyzing P300 and N2ac Responses to Various Spatial Sound Sources. In: Mori K, editor. The 7th ERA/OAE Technical Committee Meeting of Japan Audiological Society. Keio University Hospital, Tokyo, Japan: Japan Audiological Society; 2012. p. 11.

20. Rutkowski TM. EEG components separation and brain patterns elucidation method: data driven Huang-Hilbert transform and neural connectivity analysis application to a novel auditory BCI/BMI paradigm. In: Bandyopadhyay A, editor. Proceedings of The Second International Workshop on Brain Inspired Computing (BIC2012). Tsukuba, Japan: NIMS; 2012. p. 8.

21. Struzik ZR, Nakano Y, Shimizu D, Okada T, Rutkowski TM. Alpha-rhythm correlates with inspiration during dance improvisation. In: Bandyopadhyay A, editor. Proceedings of The Second International Workshop on Brain Inspired Computing (BIC2012). Tsukuba, Japan: NIMS; 2012. p. poster #1.1.

2011 (peer-reviewed & in alphabetical order)

1. Cai Z, Terasawa H, Makino S, Yamada T, Rutkowski TM. Sound Timbre and Spatial Location as Informative Cues in Auditory BCI - Brain Evoked Potential Enhancement and Involuntary Eye Movements Artifacts Suppression Approach. In: Proceedings of the Third APSIPA Annual Summit and Conference (APSIPA ASC 2011). Xi’an, China: APSIPA; 2011. p. paper 241 (6 pages). Available from: [link].

2. Furukawa K, Rutkowski TM, Hamano T, Ohmura H, Hoshi-Shiba R, Terasawa H, et al. Music performance with ‘imagery instrument’ by real-time categorisation of brain activities. In: Power of Music Abstracts - The 34th National Conference of the Musicological Society of Australia and the 2nd International Conference on Music and Emotion. Crawley, Western Australia: The University of Western Australia; 2011. p. 46–47.

3. Furukawa K, Rutkowski TM, Hoshi-Shiba R, Hamano T, Terasawa H, Okanoya K. Music performance with ’imagery instrument’ by real-time categorization of brain activities. In: ’Power of Music’ - Proceedings of The 34th National Conference of the Musicological Society of Australia and The 2nd International Conference on Music and Emotion. The University of Western Australia; 2011. p. #155.

4. Gallego-Jutgla E, Sola-Casals J, Rutkowski TM, Cichocki A. Application of Multivariate Empirical Mode Decomposition for Cleaning Eye Blinks Artifacts From EEE Signals. In: Proceedings of International Conference on Neural Computation Theory and Applications (NCTA2011). Paris, France; 2011. p. 455– 460.

5. Hamano T, Rutkowski TM, Ohmura H, Terasawa H, Hoshi-Shiba R, Okanoya K, et al.Real-Time Controllable Interface for Music Performance Using Electroencephalography. In: Proceedings of Asia Computer Music Project 2011. Tokyo Denki University: Asia Computer Music Project & Japanese Society for Sonic Arts; 2011. p. 47–52. Available from: [link].

6. Higashi H, Rutkowski TM, Washizawa Y, Cichocki A, Tanaka T. EEG Auditory Steady State Responses Classification for the Novel BCI. In: Proceedings of the 33rd Annual International Conference of the IEEE EMBS. Boston MA, USA: IEEE Press; 2011. p. 4576–4579. Available from: [link].

7. Higashi H, Rutkowski TM, Washizawa Y, Tanaka T, Cichocki A. Imagery Movement Paradigm User Adaptation Improvement with Quasi-movements Phenomenon. In: Wang R, Gu F, editors. Advances in Cognitive Neurodynamics (II). Springer Netherlands; 2011. p. 677–681. Available from: [link].

8. Kim S, Rutkowski TM. Investigating listeners’ preference and brain responses of multichannel- reproduced piano music. In: Society for Music Perception and Cognition 2011, Proceedings. University of Rochester & NAMM Foundation; 2011. p. 79.

9. Rutkowski TM, Zhao Q, Mandic DP, Cai Z, Cichocki A, Makino S, et al. New EEG components separation method: Data driven Huang-Hilbert Transform application to auditory BMI paradigm. In: 2011 Neuroscience Meeting Planner. Washington DC, USA: Society for Neuroscience; 2011. p. Program No. 627.15. Online. Available from: [link]

10. Rutkowski TM. Auditory Brain-Computer/Machine Interface Paradigms Design. In: Cooper E, Kryssanov V, Ogawa H, Brewster S, editors. Haptic and Audio Interaction Design. vol. 6851 of Lecture Notes in Computer Science. Springer Berlin / Heidelberg; 2011. p. 110–119. Available from: [link].

11. Rutkowski TM, Cichocki A, Mandic D, Nishida T. Emotional empathy transition patterns from human brain responses in interactive communication situations. AI & Society. 2011;26:301–315. Available from: [link].

12. Rutkowski TM, Mandic DP, Cao J, Przybyszewski AW. Multichannel EEG separation in HHT domain. In: The Third International Conference on Hilbert-Huang Transform: Theory and Applications. Qingdao, China: The First Institute of Oceanography, State Oceanic Administration, China; 2011. p. 78–79.

13. Rutkowski TM, Tanaka T, Cichocki A, Erickson D, Cao J, Mandic DP. Interactive component ex- traction from fEEG, fNIRS and peripheral biosignals for affective brain-machine interfacing paradigms. Computers in Human Behavior. 2011;27(5):1512 – 1518. Available from: [link].

14. Rutkowski TM, Zhao Q, Cichocki A, Tanaka T, Mandic DP. Towards Affective BCI/BMI Paradigms – Analysis of fEEG and fNIRS Brain Responses to Emotional Speech and Facial Videos. In: Wang R, Gu F, editors. Advances in Cognitive Neurodynamics (II). Springer Netherlands; 2011. p. 671–675. Available from: [link].

15. Zhao Q, Rutkowski TM, Cichocki A, Zhang L. High Resolution Common Spatial Frequency Filters for Classifying Multi-class EEG. In: Wang R, Gu F, editors. Advances in Cognitive Neurodynamics (II). Springer Netherlands; 2011. p. 683–688. Available from: [link].