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 2nd international conference on computer applications information security (ICCAIS), pp 1-4
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SP 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
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.