{"ID":2898514,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.03814","arxiv_id":"2507.03814","title":"SHAP-AAD: DeepSHAP-Guided Channel Reduction for EEG Auditory Attention Detection","abstract":"Electroencephalography (EEG)-based auditory attention detection (AAD) offers a non-invasive way to enhance hearing aids, but conventional methods rely on too many electrodes, limiting wearability and comfort. This paper presents SHAP-AAD, a two-stage framework that combines DeepSHAP-based channel selection with a lightweight temporal convolutional network (TCN) for efficient AAD using fewer channels.DeepSHAP, an explainable AI technique, is applied to a Convolutional Neural Network (CNN) trained on topographic alpha-power maps to rank channel importance, and the top-k EEG channels are used to train a compact TCN. Experiments on the DTU dataset show that using 32 channels yields comparable accuracy to the full 64-channel setup (79.21% vs. 81.06%) on average. In some cases, even 8 channels can deliver satisfactory accuracy. These results demonstrate the effectiveness of SHAP-AAD in reducing complexity while preserving high detection performance.","short_abstract":"Electroencephalography (EEG)-based auditory attention detection (AAD) offers a non-invasive way to enhance hearing aids, but conventional methods rely on too many electrodes, limiting wearability and comfort. This paper presents SHAP-AAD, a two-stage framework that combines DeepSHAP-based channel selection with a light...","url_abs":"https://arxiv.org/abs/2507.03814","url_pdf":"https://arxiv.org/pdf/2507.03814v1","authors":"[\"Rayan Salmi\",\"Guorui Lu\",\"Qinyu Chen\"]","published":"2025-07-04T21:24:40Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}