Abstract
This paper investigates the possibility of using visual imagery tasks, which are mental imagery tasks that involve imagining the images of objects perceptually without seeing them, as a control paradigm that can increase the control's dimensionality of electroencephalography (EEG)-based brain-computer interfaces. Specifically, we propose an EEG-based approach for decoding visually imagined objects by using the Choi-Williams time-frequency distribution to analyze the EEG signals in the joint time-frequency domain and extract a set of twelve time-frequency features (TFFs). The extracted TFFs are used to construct a multi-class support vector machine classifier to decode various visually imagined objects. To validate the performance of our proposed approach, we have recorded an EEG dataset for 16 healthy subjects while imagining objects that belong to four different categories, namely nature (fruits and animals), decimal digits, English alphabet (capital letters), and arrow shapes (arrows with different colors and orientations). Moreover, we have designed two performance evaluation analyses, namely the channel-based analysis and feature-based analysis, to quantify the impact of utilizing different groups of EEG electrodes that cover various regions on the scalp and the effect of reducing the dimensionality of the extracted TFFs on the performance of our proposed approach in decoding the imagined objects within each of the four categories. The experimental results demonstrate the efficacy of our proposed approach in decoding visually imagined objects. Particularly, the average decoding accuracies obtained for each of the four categories were as high as 96.67%, 93.64%, 88.95%, and 92.68%.