Abstract
Feedforward neural networks are trained to classify half-second segments of six-channel, EEG data into one of five classes corresponding to five mental tasks performed by one subject. Two and three-layer neural networks are trained on a 128-processor SIMD computer using 10-fold cross-validation and early stopping to limit over-fitting. Four representations of the EEG signals, based on autoregressive (AR) models and Fourier Transforms, are compared. Using the AR representation and averaging over consecutive segments, an average of 72% of the test segments are correctly classified; for some test sets 100% are correctly classified. Cluster arm, is of the resulting hidden-unit weight vectors suggests which electrodes and representation components are the most relevant to the classification problem.
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Anderson, C.W. (1999). Identifying mental tasks from spontaneous EEG: Signal representation and spatial analysis. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100489
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DOI: https://doi.org/10.1007/BFb0100489
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