Identification of the phase code in an EEG during gripping-force tasks: A possible alternative approach to the development of the brain-computer interfaces

• Published in: Artificial intelligence in medicine 2008-09, Vol.44 (1), p.41-49
• Main Authors: Logar, Vito, Škrjanc, Igor, Belič, Aleš, Brežan, Simon, Koritnik, Blaž, Zidar, Janez
• Format: Article
• Language: English
• Subjects: Adult; Brain - physiology; Brain–computer interface; Electroencephalography; Female; Fuzzy Logic; Gripping force; Hand Strength - physiology; Humans; Information coding; Internal Medicine; Male; Mental Processes - physiology; Models, Neurological; Other; Phase demodulation; Predictive Value of Tests; Principal Component Analysis; Reproducibility of Results; Signal Processing, Computer-Assisted; User-Computer Interface
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2008.06.003

Summary Background The subject of brain–computer interfaces (BCIs) represents a vast and still mainly undiscovered land, but perhaps the most interesting part of BCIs is trying to understand the information exchange and coding in the brain itself. According to some recent reports, the phase characteristics of the signals play an important role in the information transfer and coding. The mechanism of phase shifts, regarding the information processing, is also known as the phase coding of information. Objective The authors would like to show that electroencephalographic (EEG) signals, measured during the performance of different gripping-force control tasks, carry enough information for the successful prediction of the gripping force, as applied by the subjects, when using a methodology based on the phase demodulation of EEG data. Since the presented methodology is non-invasive it could be used as an alternative approach for the development of BCIs. Materials and methods In order to predict the gripping force from the EEG signals we used a methodology that uses subsequent signal processing methods: simplistic filtering methods, for extracting the appropriate brain rhythm; principal component analysis, for achieving the linear independence and detecting the source of the signal; and the phase-demodulation method, for extracting the phase-coded information about the gripping force. A fuzzy inference system is then used to predict the gripping force from the processed EEG data. Results The proposed methodology has clearly demonstrated that EEG signals carry enough information for a successful prediction of the subject’s performance. Moreover, a cross-validation showed that information about the gripping force is encoded in a very similar way between the subjects tested. As for the development of BCIs, considering the computational time to pre-process the data and train the fuzzy model, a real-time online analysis would be possible if the real-time non-causal limitations of the methodology could be overcome. Conclusion The study has shown that phase coding in the human brain is a possible mechanism for information coding or transfer during visuo-motor tasks, while the phase-coded content about the gripping forces can be successfully extracted using the phase-demodulation approach. Since the methodology has proven to be appropriate for the case of this study it could also be used as an alternative approach for the development of BCIs for similar tasks.