Abdul Rehman Satti

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The non-stationary nature of the electroencephalo-gram (EEG) poses a major challenge for the successful operation of a brain-computer interface (BCI) when deployed over multiple sessions. The changes between the early training measurements and the proceeding multiple sessions can originate as a result of alterations in the subject's brain process, new(More)
This paper shows for the first time how a popular and successful filtering approach, known as the common spatial patterns (CSP) approach, compares to the neural time series prediction preprocessing (NTSPP) approach when applied in a 2-class EEG-based brain-computer interface (BCI), either using 2 or 60 EEG channels. Additionally, a novel NTSPP-CSP(More)
2) correlation coefficient rather than assessing overall systems performance via performance measure such as classification accuracy. Experimental results utilizing eight subjects are presented which demonstrate the effectiveness of the proposed methods for fast & efficient user-specific tuned BCI system.
1. Neural Time Series Prediction Preprocessing (NTSPP) was applied to all signals all subjects using the self-organizing fuzzy neural network (SOFNN). In a few cases subject performed slightly better without NTSPP however for the majority of subjects significant increases in the average accuracy were provided by NTSPP. NTSPP has been extended to work with(More)
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