SpellerSSL: Self-Supervised Learning with P300 Aggregation for Speller BCIs

Name: SpellerSSL: Self-Supervised Learning with P300 Aggregation for Speller BCIs
Uploaded: 2026-10-15T09:55:09.410683+00:00
Duration: 240 s
Description: SpellerSSL improves EEG-based P300 spellers by combining self-supervised learning with a specific signal aggregation strategy. The framework uses a customized U-Net for pretraining on EEG data to learn robust representations. This method enhances signal quality, leading to higher character recognition rates and significantly reducing the time needed for system calibration.

SpellerSSL improves EEG-based P300 spellers by combining self-supervised learning with a specific signal aggregation strategy. The framework uses a customized U-Net for pretraining on EEG data to learn robust representations. This method enhances signal quality, leading to higher character recognition rates and significantly reducing the time needed for system calibration.

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