Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain–Computer Interface Performance
(1) Background: in the field of motor-imagery brain–computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which le...
Main Authors: | Ilaria Siviero, Gloria Menegaz, Silvia Francesca Storti |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-08-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/23/17/7520 |
Similar Items
-
Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces
by: Seraphim S. Moumgiakmas, et al.
Published: (2022-04-01) -
A multi-band centroid contrastive reconstruction fusion network for motor imagery electroencephalogram signal decoding
by: Jiacan Xu, et al.
Published: (2023-11-01) -
MOTOR IMAGERY EEG SIGNAL PROCESSING AND CLASSIFICATION USING MACHINE LEARNING APPROACH
by: S. R. Sreeja, et al.
Published: (2018-08-01) -
A Decoding Method Using Riemannian Local Linear Feature Construction for a Lower-Limb Motor Imagery Brain–Computer Interface System
by: Yao Hou, et al.
Published: (2023-11-01) -
Optimal Sensor Set for Decoding Motor Imagery from EEG
by: Arnau Dillen, et al.
Published: (2023-03-01)