An EEG classifier using channel-pair log-energy features and a bi-LSTM reports 98.66% accuracy for speech versus music, 61.59% for four genres, and 96.96% for musical taste, but the random split of overlapping trials inflates the results.
In: 2019 3rd international conference on trends in electronics and informatics (ICOEI), pp 1248-1253
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Energy-based features and bi-LSTM neural network for EEG-based music and voice classification
An EEG classifier using channel-pair log-energy features and a bi-LSTM reports 98.66% accuracy for speech versus music, 61.59% for four genres, and 96.96% for musical taste, but the random split of overlapping trials inflates the results.