Objective: Despite the effective application of deep learning in brain-computer interface (BCI) systems, the successful execution of this technique especially for inter-subject classification in cognitive BCI has not been accomplished yet. In this paper, we propose a framework based on deep convolutional neural network (CNN) to detect attentive mental state from single-channel raw electroencephalography (EEG) data. Approach: We develop an end-to-end deep CNN to decode the attentional information from EEG time-series. We also explore the consequence of input representations on the performance of deep CNN by feeding three different EEG representations into the network. To ensure the practical application of the proposed framework and avoid time-consuming re-trainings, we perform inter-subject transfer learning techniques as classification strategy. Eventually, to interpret the learned attentional patterns, we visualize and analyze the network perception of attention and nonattention classes. Main results: The average classification accuracy is 79.26% with only 15.83% of 120 subjects having the accuracy below 70% (a generally accepted threshold for BCI). This is while with inter-subject approach, it is literally hard to output high classification accuracy. This end-to-end classification framework surpasses the conventional classification methods for attention detection. The visualization results validate that the learned patterns from raw data are meaningful. Significance: This framework significantly improves the attention detection accuracy with inter-subject classification. Moreover, this study sheds light into the research on end-to-end learning; the proposed network is capable to learn from raw data with the least amount of pre-processing which in turn eliminates the extensive computational load of time-consuming data preparation and feature extraction.

The performance of a classifier in a brain-computer interface (BCI) system is highly dependent on the quality and quantity of training data. Typically, the training data are collected in a laboratory where the users perform tasks in a controlled environment. However, users' attention may be diverted in reallife BCI applications and this may decrease the performance of the classifier. To improve the robustness of the classifier, additional data can be acquired in such conditions, but it is not practical to record electroencephalogram (EEG) data over several long calibration sessions. A potentially time-and cost-efficient solution is artificial data generation. Hence, in this study, we proposed a framework based on the deep convolutional generative adversarial networks (DCGANs) for generating artificial EEG to augment the training set in order to improve the performance of a BCI classifier. To make a comparative investigation, we designed a motor task experiment with diverted and focused attention conditions. We used an end-to-end deep convolutional neural network for classification between movement intention and rest using the data from 14 subjects. The results from the leaveone subject-out (LOO) classification yielded baseline accuracies of 73.04% for diverted attention and 80.09% for focused attention without data augmentation. Using the proposed DCGANs-based framework for augmentation, the results yielded a significant improvement of 7.32% for diverted attention ( p < 0.01) and 5.45% for focused attention ( p < 0.01). In addition, we implemented the method on the data set IVa from BCI competition III to distinguish different motor imagery tasks. The proposed method increased the accuracy by 3.57% ( p < 0.02). This study shows that using GANs for EEG augmentation can significantly improve BCI performance, especially in real-life applications, whereby users' attention may be diverted.