Interpersonal communication is based on questions and answers, and the most useful and simplest case is the binary "yes or no" question and answer. The purpose of this study is to show that it is possible to decode intentions on "yes" or "no" answers from multichannel single-trial electroencephalograms, which were recorded while covertly answering to self-referential questions with either "yes" or "no." The intention decoding algorithm consists of a common spatial pattern and support vector machine, which are employed for the feature extraction and pattern classification, respectively, after dividing the overall time-frequency range into subwindows of 200 ms × 2 Hz. The decoding accuracy using the information within each subwindow was investigated to find useful temporal and spectral ranges and found to be the highest for 800-1200 ms in the alpha band or 200-400 ms in the theta band. When the features from multiple subwindows were utilized together, the accuracy was significantly increased up to ∼86%. The most useful features for the "yes/no" discrimination was found to be focused in the right frontal region in the theta band and right centroparietal region in the alpha band, which may reflect the violation of autobiographic facts and higher cognitive load for "no" compared to "yes." Our task requires the subjects to answer self-referential questions just as in interpersonal conversation without any self-regulation of the brain signals or high cognitive efforts, and the "yes" and "no" answers are decoded directly from the brain activities. This implies that the "mind reading" in a true sense is feasible. Beyond its contribution in fundamental understanding of the neural mechanism of human intention, the decoding of "yes" or "no" from brain activities may eventually lead to a natural brain-computer interface.
Bibliographical noteFunding Information:
-is research was supported by the Brain Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (no. 2017M3C7A1029485).
© 2019 Jeong Woo Choi and Kyung Hwan Kim.
All Science Journal Classification (ASJC) codes
- Computer Science(all)