Robust extraction of P300 using constrained ICA for BCI applications |
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Authors: | Khan Ozair Idris Farooq Faisal Akram Faraz Choi Mun-Taek Han Seung Moo Kim Tae-Seong |
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Institution: | (1) Department of Biomedical Engineering, Kyung Hee University, Yongin, Republic of Korea;(2) Center for Intelligent Robotics, Korea Institute of Science and Technology, Seoul, Republic of Korea; |
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Abstract: | P300 is a positive event-related potential used by P300-brain computer interfaces (BCIs) as a means of communication with
external devices. One of the main requirements of any P300-based BCI is accuracy and time efficiency for P300 extraction and
detection. Among many attempted techniques, independent component analysis (ICA) is currently the most popular P300 extraction
technique. However, since ICA extracts multiple independent components (ICs), its use requires careful selection of ICs containing
P300 responses, which limits the number of channels available for computational efficiency. Here, we propose a novel procedure
for P300 extraction and detection using constrained independent component analysis (cICA) through which we can directly extract
only P300-relevant ICs. We tested our procedure on two standard datasets collected from healthy and disabled subjects. We
tested our procedure on these datasets and compared their respective performances with a conventional ICA-based procedure.
Our results demonstrate that the cICA-based method was more reliable and less computationally expensive, and was able to achieve
97 and 91.6% accuracy in P300 detection from healthy and disabled subjects, respectively. In recognizing target characters
and images, our approach achieved 95 and 90.25% success in healthy and disabled individuals, whereas use of ICA only achieved
83 and 72.25%, respectively. In terms of information transfer rate, our results indicate that the ICA-based procedure optimally
performs with a limited number of channels (typically three), but with a higher number of available channels (>3), its performance
deteriorates and the cICA-based one performs better. |
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