Emotion　recognition　from　physiological　signals　is　a　crucial　area　in　affective　computing.　However,　traditional　CNN　models　face　challenges　in　accuracy　and　efficiency.　This　paper　proposes　a　lightweight　IGC-CNN　model　that　integrates　interleaved　group　convolutions　with　the　LeNet-5　network.　Experimental　results　using　EEG,　EMG,　and　EDA　signals　collected　across　happiness,　sadness,　and　fear　states　show　that　IGC-CNN　achieves　an　average　accuracy　of　94.74%,　outperforming　traditional　CNNs　by　10.06%.　Statistical　analysis　confirms　the　significance　of　this　improvement　(P　©　2024　SPIE.