A diagonal masking self-attention-based multi-scale network for motor imagery classification.
J Neural Eng
; 21(3)2024 Jun 13.
Article
en En
| MEDLINE
| ID: mdl-38834056
ABSTRACT
Objective. Electroencephalography (EEG)-based motor imagery (MI) is a promising paradigm for brain-computer interface (BCI), but the non-stationarity and low signal-to-noise ratio of EEG signals make it a challenging task.Approach. To achieve high-precision MI classification, we propose a Diagonal Masking Self-Attention-based Multi-Scale Network (DMSA-MSNet) to fully develop, extract, and emphasize features from different scales. First, for local features, a multi-scale temporal-spatial block is proposed to extract features from different receptive fields. Second, an adaptive branch fusion block is specifically designed to bridge the semantic gap between these coded features from different scales. Finally, in order to analyze global information over long ranges, a diagonal masking self-attention block is introduced, which highlights the most valuable features in the data.Main results. The proposed DMSA-MSNet outperforms state-of-the-art models on the BCI Competition IV 2a and the BCI Competition IV 2b datasets.Significance. Our study achieves rich information extraction from EEG signals and provides an effective solution for MI classification.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Electroencefalografía
/
Interfaces Cerebro-Computador
/
Imaginación
Límite:
Humans
Idioma:
En
Revista:
J Neural Eng
Asunto de la revista:
NEUROLOGIA
Año:
2024
Tipo del documento:
Article