Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG - IMT Mines Alès Access content directly
Conference Papers Year : 2022

Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG

Zaineb Ajra
  • Function : Author
  • PersonId : 1168126
  • IdHAL : zaineb-ajra
Binbin Xu
  • Function : Author
  • PersonId : 747446
  • IdHAL : binbinxu
Gérard Dray
Jacky Montmain
S. Perrey

Abstract

In recent years, neuroscientists have been interested to the development of brain-computer interface (BCI) devices. Patients with motor disorders may benefi t from BCIs as a means of communication and for the restoration of motor functions. Electroencephalography (EEG) is one of most used for evaluating the neuronal activity. In many computer vision applications, deep neural networks (DNN) show significant advantages. Towards to ultimate usage of DNN, we present here a shallow neural network that uses mainly two convolutional neural network (CNN) layers, with relatively few parameters and fast to learn spectral-temporal features from EEG. We compared this models to three other neural network models with different depths applied to a mental arithmetic task using eye-closed state adapted for patients suffering from motor disorders and a decline in visual functions. Experimental results showed that the shallow CNN model outperformed all the other models and achieved the highest classifi cation accuracy of 90.68%. It’s also more robust to deal with cross-subject classifi cation issues: only 3% standard deviation of accuracy instead of 15.6% from conventional method.
Fichier principal
Vignette du fichier
Ajra2022Mental.pdf (763.96 Ko) Télécharger le fichier
Origin Publication funded by an institution

Dates and versions

hal-03711249 , version 1 (13-09-2022)

Identifiers

Cite

Zaineb Ajra, Binbin Xu, Gérard Dray, Jacky Montmain, S. Perrey. Mental arithmetic task classification with convolutional neural network based on spectral-temporal features from EEG. EMBS 2022 - 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jul 2022, Glasgow, United Kingdom. pp.52-55, ⟨10.1109/EMBC48229.2022.9870887⟩. ⟨hal-03711249⟩
131 View
110 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More