运动表象
脑-机接口
判别式
脑电图
计算机科学
人工智能
接口(物质)
模式识别(心理学)
特征(语言学)
神经生理学
语音识别
心理学
神经科学
语言学
哲学
气泡
最大气泡压力法
并行计算
作者
Sadaf Moaveninejad,Valentina D’Onofrio,Franca Tecchio,Francesco Ferracuti,Sabrina Iarlori,Andrea Monteriù,Camillo Porcaro
标识
DOI:10.1016/j.cmpb.2023.107944
摘要
The brain-computer interface (BCI) technology acquires human brain electrical signals, which can be effectively and successfully used to control external devices, potentially supporting subjects suffering from motor impairments in the interaction with the environment. To this aim, BCI systems must correctly decode and interpret neurophysiological signals reflecting the intention of the subjects to move. Therefore, the accurate classification of single events in motor tasks represents a fundamental challenge in ensuring efficient communication and control between users and BCIs. Movement-associated changes in electroencephalographic (EEG) sensorimotor rhythms, such as event-related desynchronization (ERD), are well-known features of discriminating motor tasks. Fractal dimension (FD) can be used to evaluate the complexity and self-similarity in brain signals, potentially providing complementary information to frequency-based signal features. In the present work, we introduce FD as a novel feature for subject-independent event classification, and we test several machine learning (ML) models in behavioural tasks of motor imagery (MI) and motor execution (ME). Our results show that FD improves the classification accuracy of ML compared to ERD. Furthermore, unilateral hand movements have higher classification accuracy than bilateral movements in both MI and ME tasks. These results provide further insights into subject-independent event classification in BCI systems and demonstrate the potential of FD as a discriminative feature for EEG signals.
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