Prediction of Attention in Autism from Single-Trial EEG Using Artificial Neural Networks Matthew Belmonte ABSTRACT: Two-layer and three-layer feedforward artificial neural networks were trained to predict behvioural performance from singletrial EEG in autistic and normal subjects in a task involving response to rare stimuli and shifting of attention between vision and audition. Eyeblink artefacts were removed from the data using a frequency-domain filter. Performances of the networks on separate test sets varied across subjects but were usually at least 80%. The networks usually converged faster and attained a somewhat greater level of performance when input was presented in the frequency domain instead of in the time domain. Analysis of the network's failures in classifying the autistic auditory data turned up a variable response in which N270 and P700 were only occasionally present. DESCRIPTORS: neural networks, EEG, ERP, P3, autism, attention, artefact correction, eyeblink Address for correspondence: mkb4@cornell.edu choice of network architecture. Generalised ANNs also have the potenti[al to perform better than linear methods such as discriminant analysis because of their capacity to implement nonlinear boundaries in the problem .,;pace. A completely connected, feed-forward ANN such as the ones used in this study consists of a set of neuron-like
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Prediction of attention in autism from single-trial EEG using artificial neural networks
Belmonte, Matthew
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, Volume 17 (2)
Association for Computing Machinery – Aug 1, 1997
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