Reducing Response Time in Motor Imagery Using A Headband and Deep Learning

Sensors

MaríaJosé Rodríguez-Fórtiz¹  FranciscoM. Garcia-Moreno¹  Maria Bermudez-Edo¹  JoséLuis Garrido¹ 

[1] Department of Software Engineering, Computer Science School, University of Granada, 18014 Granada, Spain;

关键词: neural networks;    deep learning;    motor imagery;    wearable;    EEG;    BCI;   

DOI  :  10.3390/s20236730

来源: DOAJ

【 摘 要 】

Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with low mobility. However, the regular brain computer interfaces (BCI) capturing the EEG signals usually require intrusive devices and cables linked to machines. Recently, some commercial low-intrusive BCI headbands have appeared, but with less electrodes than the regular BCIs. Some works have proved the ability of the headbands to detect basic motor imagery. However, all of these works have focused on the accuracy of the detection, using session sizes larger than 10 s, in order to improve the accuracy. These session sizes prevent actuators using the headbands to interact with the user within an adequate response time. In this work, we explore the reduction of time-response in a low-intrusive device with only 4 electrodes using deep learning to detect right/left hand motion imagery. The obtained model is able to lower the detection time while maintaining an acceptable accuracy in the detection. Our findings report an accuracy above 83.8% for response time of 2 s overcoming the related works with both low- and high-intrusive devices. Hence, our low-intrusive and low-cost solution could be used in an interactive system with a reduced response time of 2 s.

【 授权许可】

Unknown