Brain–computer interface analysis of a dynamic visuo-motor task

• Published in: Artificial intelligence in medicine 2011-01, Vol.51 (1), p.43-51
• Main Authors: Logar, Vito, Belič, Aleš
• Format: Article
• Language: English
• Subjects: Adult; Artificial Intelligence; Brain - physiology; Brain Waves; Brain-information decoding; Brain–computer interface; Dynamic visuo-motor task; Electroencephalography; Fuzzy Logic; Humans; Internal Medicine; Male; Models, Biological; Motor Activity; Other; Principal Component Analysis; Psychomotor Performance; Signal Processing, Computer-Assisted; Software; Time Factors; User-Computer Interface; Wrist - innervation; Wrist-movement prediction; Young Adult
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2010.10.004

Abstract Background The area of brain–computer interfaces (BCIs) represents one of the more interesting fields in neurophysiological research, since it investigates the development of the machines that perform different transformations of the brain's “thoughts” to certain pre-defined actions. Experimental studies have reported some successful implementations of BCIs; however, much of the field still remains unexplored. According to some recent reports the phase coding of informational content is an important mechanism in the brain's function and cognition, and has the potential to explain various mechanisms of the brain's data transfer, but it has yet to be scrutinized in the context of brain–computer interface. Therefore, if the mechanism of phase coding is plausible, one should be able to extract the phase-coded content, carried by brain signals, using appropriate signal-processing methods. In our previous studies we have shown that by using a phase-demodulation-based signal-processing approach it is possible to decode some relevant information on the current motor action in the brain from electroencephalographic (EEG) data. Objective In this paper the authors would like to present a continuation of their previous work on the brain-information-decoding analysis of visuo-motor (VM) tasks. The present study shows that EEG data measured during more complex, dynamic visuo-motor (dVM) tasks carries enough information about the currently performed motor action to be successfully extracted by using the appropriate signal-processing and identification methods. The aim of this paper is therefore to present a mathematical model, which by means of the EEG measurements as its inputs predicts the course of the wrist movements as applied by each subject during the task in simulated or real time (BCI analysis). However, several modifications to the existing methodology are needed to achieve optimal decoding results and a real-time, data-processing ability. The information extracted from the EEG could, therefore, be further used for the development of a closed-loop, non-invasive, brain–computer interface. Materials and methods For the case of this study two types of measurements were performed, i.e., the electroencephalographic (EEG) signals and the wrist movements were measured simultaneously, during the subject's performance of a dynamic visuo-motor task. Wrist-movement predictions were computed by using the EEG data-processing methodology of double brain-rhythm filte