Emotion classification using EEG signals based on tunable‐ Q wavelet transform

Abstract

Emotion is a most instinctive feeling of a human. Emotion classification finds application in brain–computer interface systems for the assistance of disabled persons. To recognise the emotional state, electroencephalogram (EEG) signal plays a vital role because it provides immediate response to every state of change in the human brain. Here, the utility of tunable‐ Q wavelet transform (TQWT) is explored for the classification of different emotions EEG signals. TQWT decomposes EEG signal into subbands and time‐domain features are extracted from subbands. The extracted features are used as an input to extreme learning machine classifier for the classification of happy, fear, sad, and relax emotions. Experimental results of the proposed method show better four emotions classification performance when compared with the other existing methods.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 60 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.