Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to become more intelligent. Due to the outstanding applications of emotion recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling detection, etc., emotion recognition has become successful in attracting the recent hype of AI-empowered research. Therefore, numerous studies have been conducted driven by a range of approaches, which demand a systematic review of methodologies used for this task with their feature sets and techniques. It will facilitate the beginners as guidance towards composing an effective emotion recognition system. In this article, we have conducted a rigorous review on the state-of-the-art emotion recognition systems, published in recent literature, and summarized some of the common emotion recognition steps with relevant definitions, theories, and analyses to provide key knowledge to develop a proper framework. Moreover, studies included here were dichotomized based on two categories: i) deep learning-based, and ii) shallow machine learning-based emotion recognition systems. The reviewed systems were compared based on methods, classifier, the number of classified emotions, accuracy, and dataset used. An informative comparison, recent research trends, and some recommendations are also provided for future research directions.

In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain–computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research.

Blindness prevents a person from gaining knowledge of the surrounding environment and makes unassisted navigation, object recognition, obstacle avoidance, and reading tasks a major challenge. In this work, we propose a novel visual aid system for the completely blind. Because of its low cost, compact size, and ease-of-integration, Raspberry Pi 3 Model B+ has been used to demonstrate the functionality of the proposed prototype. The design incorporates a camera and sensors for obstacle avoidance and advanced image processing algorithms for object detection. The distance between the user and the obstacle is measured by the camera as well as ultrasonic sensors. The system includes an integrated reading assistant, in the form of the image-to-text converter, followed by an auditory feedback. The entire setup is lightweight and portable and can be mounted onto a regular pair of eyeglasses, without any additional cost and complexity. Experiments are carried out with 60 completely blind individuals to evaluate the performance of the proposed device with respect to the traditional white cane. The evaluations are performed in controlled environments that mimic real-world scenarios encountered by a blind person. Results show that the proposed device, as compared with the white cane, enables greater accessibility, comfort, and ease of navigation for the visually impaired.

Hand gesture recognition is one of the most widely explored areas under the human–computer interaction domain. Although various modalities of hand gesture recognition have been explored in the last three decades, in recent years, due to the availability of hardware and deep learning algorithms, hand gesture recognition research has attained renewed momentum. In this paper, we evaluate the effectiveness of a low-cost dataglove for classifying hand gestures in the light of deep learning. We have developed a cost-effective dataglove using five flex sensors, an inertial measurement unit, and a powerful microcontroller for onboard processing and wireless connectivity. We have collected data from 25 subjects for 24 static and 16 dynamic American sign language gestures for validating our system. Moreover, we proposed a novel Spatial Projection Image-based technique for dynamic hand gesture recognition. We also explored a parallel-path neural network architecture for handling multimodal data more effectively. Our method produced an F1-score of 82.19% for static gestures and 97.35% for dynamic gestures from a leave-one-out-cross-validation approach. Overall, this study demonstrates the promising performance of a generalized hand gesture recognition technique in hand gesture recognition. The dataset used in this work has been made publicly available.