Mental attention classification using electroencephalogram (EEG) has become an important aspect of brain-computer interface (BCI) devices, especially with the growing use of single-channel EEG wearable devices. Nonetheless, the challenges of limited spatial information, vulnerability to noise, and the inability to repr...
Padakanti Swapna, Ravichander Janapati· 2026 6th International Confe...· 0 citations
Electroencephalography (EEG) is a key research field in affective computing and brain-computer interface applications for the recognition of emotion. But the non-stationary and complex nature of EEG signals presents a great challenge for emotion classification. In this paper, a novel hybrid feature learning framework f...
P. Mula, V. Malathy, Mohammad Farukh Hashmi et al.· 2026 7th International Confe...· 0 citations
EEG based Emotion recognition has gained a lot of interest in the domain of Affective Computing, Healthcare and Human-Computer Interaction. Emotion classification in EEG signals remains difficult, though, because of their nonlinearity, noise and dependence on the specific person. In this paper, we suggest an Adaptive M...
P. Mula, V. Malathy, Mohammad Farukh Hashmi et al.· 2026 7th International Confe...· 0 citations
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