The brain-computer interface (BCI) is a cutting-edge technology that has the potential to change the world. Electroencephalogram (EEG) motor imagery (MI) signal has been used extensively in many BCI applications to assist disabled people, control devices or environments, and even augment human capabilities. However, the limited performance of brain signal decoding is restricting the broad growth of the BCI industry. In this article, we propose an attention-based temporal convolutional network (ATCNet) for EEG-based motor imagery classification. The ATCNet model utilizes multiple techniques to boost the performance of MI classification with a relatively small number of parameters. ATCNet employs scientific machine learning to design a domain-specific deep learning model with interpretable and explainable features, multihead self-attention to highlight the most valuable features in MI-EEG data, temporal convolutional network to extract high-level temporal features, and convolutional-based sliding window to augment the MI-EEG data efficiently. The proposed model outperforms the current state-of-the-art techniques in the BCI Competition IV-2a dataset with an accuracy of 85.38% and 70.97% for the subject-dependent and subject-independent modes, respectively.

Electroencephalography-based motor imagery (EEG-MI) classification is a critical component of the brain-computer interface (BCI), which enables people with physical limitations to communicate with the outside world via assistive technology. Regrettably, EEG decoding is challenging because of the complexity, dynamic nature, and low signal-to-noise ratio of the EEG signal. Developing an end-to-end architecture capable of correctly extracting EEG data’s high-level features remains a difficulty. This study introduces a new model for decoding MI known as a Multi-Branch EEGNet with squeeze-and-excitation blocks (MBEEGSE). By clearly specifying channel interdependencies, a multi-branch CNN model with attention blocks is employed to adaptively change channel-wise feature responses. When compared to existing state-of-the-art EEG motor imagery classification models, the suggested model achieves good accuracy (82.87%) with reduced parameters in the BCI-IV2a motor imagery dataset and (96.15%) in the high gamma dataset.

In recent years, the contributions of deep learning have had a phenomenal impact on electroencephalography-based brain-computer interfaces. While the decoding accuracy of electroencephalography signals has continued to increase, the process has caused deep learning models to continuously expand in terms of size and computational resource requirements. However, due to their increased size and computational requirements, it has become difficult to embed, store, and execute deep learning models for artificial intelligence of things, cloud-based, or edge devices used in rehabilitation. Hence, this article proposes a novel deep learning-based lightweight model based on attention-inception convolutional neural network and long- short-term memory. The proposed model achieves excellent accuracy on public competition datasets while requiring few parameters and low computational time. Using the BCI competition IV 2a dataset and the high gamma dataset, the proposed model achieved 82.8% and 97.1% accuracies, respectively.

Brain–computer interface (BCI) is an innovative technology that utilizes artificial intelligence (AI) and wearable electroencephalography (EEG) sensors to decode brain signals and enhance the quality of life. EEG-based motor imagery (MI) brain signal is used in many BCI applications, including smart healthcare, smart homes, and robotics control. However, the restricted ability to decode brain signals is a major factor preventing BCI technology from expanding significantly. In this study, we introduce a dynamic attention temporal convolutional network (D-ATCNet) for decoding EEG-based MI signals. The D-ATCNet model uses dynamic convolution (Dy-conv) and multilevel attention to enhance the performance of MI classification with a relatively small number of parameters. D-ATCNet has two main blocks: 1) dynamic and 2) temporal convolution. Dy-conv uses multilevel attention to encode low-level MI-EEG information and temporal convolution uses shifted window with self-attention to extract high-level temporal information from the encoded signal. The proposed model performs better than the existing methods with an accuracy of 71.3% for subject independent and 87.08% for subject dependent using the BCI competition IV-2a data set.

This paper presents a novel Arabic Sign Language (ArSL) recognition system, using selected 2D hands and body key points from successive video frames. The system recognizes the recorded video signs, for both signer dependent and signer independent modes, using the concatenation of a 3D CNN skeleton network and a 2D point convolution network. To accomplish this, we built a new ArSL video-based sign database. We will present the detailed methodology of recording the new dataset, which comprises 80 static and dynamic signs that were repeated five times by 40 signers. The signs include Arabic alphabet, numbers, and some daily use signs. To facilitate building an online sign recognition system, we introduce the inverse efficiency score to find a sufficient optimal number of successive frames for the recognition decision, in order to cope with a near real-time automatic ArSL system, where tradeoff between accuracy and speed is crucial to avoid delayed sign classification. For the dependent mode, best results were obtained for dynamic signs with an accuracy of 98.39%, and 88.89% for the static signs, and for the independent mode, we obtained for the dynamic signs an accuracy of 96.69%, and 86.34% for the static signs. When both the static and dynamic signs were mixed and the system trained with all the signs, accuracies of 89.62% and 88.09% were obtained in the signer dependent and signer independent modes respectively.

There has been a growing interest in objective assessment of speech in dysphonic patients for the classification of the type and severity of voice pathologies using automatic speech recognition (ASR). The aim of this work was to study the accuracy of the conventional ASR system (with Mel frequency cepstral coefficients (MFCCs) based front end and hidden Markov model (HMM) based back end) in recognizing the speech characteristics of people with pathological voice. The speech samples of 62 dysphonic patients with six different types of voice disorders and 50 normal subjects were analyzed. The Arabic spoken digits were taken as an input. The distribution of the first four formants of the vowel /a/ was extracted to examine deviation of the formants from normal. There was 100% recognition accuracy obtained for Arabic digits spoken by normal speakers. However, there was a significant loss of accuracy in the classifications while spoken by voice disordered subjects. Moreover, no significant improvement in ASR performance was achieved after assessing a subset of the individuals with disordered voices who underwent treatment. The results of this study revealed that the current ASR technique is not a reliable tool in recognizing the speech of dysphonic patients.