A deep learning method for classification of steady-state visual evoked potentials in a brain-computer interface speller

ABSTRACT

The main drawback of SSVEP-based BCIs is the lack of a classifier that categorizes SSVEPs with high accuracy and Information Transfer Rate (ITR). Addressing this, we proposed a deep convolutional neural network (CNN) for classifying a 40-class SSVEP. Time windows of length 2 and 3.5 seconds were used for training and testing the model by leave-one-subject-out cross-validation, using nine, three, and single-channel EEG. The proposed model reached 88.5% average accuracy for the nine-channel EEG with the mean and max ITR of 72 and 91.23 bpm, respectively. It outperformed the previous deep learning methods for SSVEP-based BCIs, in terms of accuracy and ITR. In the three-channel experiment the mean accuracy and ITR were 76.02% and 40.1 bpm. In single-channel implementation, O₁ channel achieved 77.38 % average accuracy (highest) and the mean ITR was 57.51 bpm. The model showed promising performance to put this technology forward and make it more practical.

KEYWORDS:

• Electroencephalogram (EEG)
• Brain-Computer interface (BCI) speller
• Steady-State visual evoked potential (SSVEP)
• Convolutional neural network (CNN)
• Deep learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. https://numpy.org

2. https://www.tensorflow.org/

3. https://keras.io

4. https://matplotlib.org

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.