An end-to-end CNN with attentional mechanism applied to raw EEG in a BCI classification task

Abstract

Objective. Motor-imagery (MI) classification base on electroencephalography (EEG) has been long studied in neuroscience and more recently widely used in healthcare applications such as mobile assistive robots and neurorehabilitation. In particular, EEG-based MI classification methods that rely on convolutional neural networks (CNNs) have achieved relatively high classification accuracy. However, naively training CNNs to classify raw EEG data from all channels, especially for high-density EEG, is computationally demanding and requires huge training sets. It often also introduces many irrelevant input features, making it difficult for the CNN to extract the informative ones. This problem is compounded by a dearth of training data, which is particularly acute for MI tasks, because these are cognitively demanding and thus fatigue inducing. Approach. To address these issues, we proposed an end-to-end CNN-based neural network with attentional mechanism together with different data augmentation (DA) techniques. We tested it on two benchmark MI datasets, brain–computer interface (BCI) competition IV 2a and 2b. In addition, we collected a new dataset, recorded using high-density EEG, and containing both MI and motor execution (ME) tasks, which we share with the community. Main results. Our proposed neural-network architecture outperformed all state-of-the-art methods that we found in the literature, with and without DA, reaching an average classification accuracy of 93.6% and 87.83% on BCI 2a and 2b, respectively. We also directly compare decoding of MI and ME tasks. Focusing on MI classification, we find optimal channel configurations and the best DA techniques as well as investigate combining data across participants and the role of transfer learning. Significance. Our proposed approach improves the classification accuracy for MI in the benchmark datasets. In addition, collecting our own dataset enables us to compare MI and ME and investigate various aspects of EEG decoding critical for neuroscience and BCI.

Realizing the Application of EEG Modeling in BCI Classification: Based on a Conditional GAN Converter

“…In future work, more attention should be paid to the diversity of the converted EEGs. The attentional mechanism can be involved ( Lashgari et al, 2021 ), to focus more on the main characteristics so that to enable more randomness in other secondary characteristics.…”

A Cross-Space CNN With Customized Characteristics for Motor Imagery EEG Classification

“…Besides, it is worth mentioning that CNN is considered to be one of the most promising networks for solving classification problems in the MI-based BCI field [44]. For example, end-to-end CNN was used to extract temporal and spatial information from EEG signals [45]. Multi-branch CNN was used to classify three-dimensional representations of EEG signals [46].…”

An Auxiliary Synthesis Framework for Enhancing EEG-Based Classification With Limited Data

“…Depthwise separable convolution reduces the parameters while maintain the decoding performance simultaneously [47], [48]. Considering the efficacy and generalizability of deep learning on EEG-based decoding of motor imagery, the Squeeze-and-Excitement (SE) attention mechanism is added to improve the classification performance by changing the weights of different channels [27], [49]. These weighted spatialtemporal features are finally classified by a fully connected layer.…”

“…According to the investigations of Lashgari and He et al [20], [24], common augmentation methods used in this area include cropping (sliding window), adding noise, and generative adversarial networks (GAN) [25]. Other methods such as recombination of segmentation [26], Fourier transform [27], synthetic minority over-sampling technique (SMOTE) can also be found in some BCI studies [28].…”