Brain-Controlled Vehicle (BCV) is an already established technology usually designed for disabled patients. This review focuses on the most relevant topics on brain-controlled vehicles, with a special reference to the terrestrial BCV (e.g., the mobile car, car simulator, real car, graphical and gaming car) and the aerial BCV, also called BCAV (e.g., real quadcopters, drones, fixed wings, graphical helicopter, and aircraft) controlled by using bio-signals, such as electroencephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG). For instance, EEG-based algorithms detect patterns from the motor imaginary cortex area of the brain for intention detection, patterns like event-related desynchronization/event-related synchronization, steady-state visually evoked potentials, P300, and generated local evoked potential patterns. We have identified that the reported best-performing approaches employ machine learning and artificial intelligence optimization methods, namely support vector machine, neural network, linear discriminant analysis, k-nearest neighbor, k-means, water drop optimization, and chaotic tug of war. We considered the following metrics to analyze the efficiency of the different methods: type and combination of bio-signals, time response, and accuracy values with statistical analysis. The present work provides an extensive literature review of the key findings of the past ten years, indicating future perspectives in the field.

This paper presents the first comprehensive tutorial on a promising research field located at the frontier of two well-established domains, neurosciences and wireless communications, motivated by the ongoing efforts to define the Sixth Generation of Mobile Networks (6G). In particular, this tutorial first provides a novel integrative approach that bridges the gap between these two seemingly disparate fields. Then, we present the state-of-the-art and key challenges of these two topics. In particular, we propose a novel systematization that divides the contributions into two groups, one focused on what neurosciences will offer to future wireless technologies in terms of new applications and systems architecture (Neurosciences for Wireless Networks), and the other on how wireless communication theory and next-generation wireless systems can provide new ways to study the brain (Wireless Networks for Neurosciences). For the first group, we explain concretely how current scientific understanding of the brain would enable new applications within the context of a new type of service that we dub brain-type communications and that has more stringent requirements than human- and machine-type communication. In this regard, we expose the key requirements of brain-type communication services and discuss how future wireless networks can be equipped to deal with such services. Meanwhile, for the second group, we thoroughly explore modern communication systems paradigms, including Internet of Bio-Nano Things and wireless-integrated brain-machine interfaces, in addition to highlighting how complex systems tools can help bridging the upcoming advances of wireless technologies and applications of neurosciences. Brain-controlled vehicles are then presented as our case study to demonstrate for both groups the potential created by the convergence of neurosciences and wireless communications, probably in 6G. In summary, this tutorial is expected to provide a largely missing articulation between neurosciences and wireless communications while delineating concrete ways to move forward in such an interdisciplinary endeavor.

Several methods utilizing common spatial pattern (CSP) algorithm have been presented for improving the identification of imagery movement patterns for brain computer interface applications. The present study focuses on improving a CSP-based algorithm for detecting the motor imagery movement patterns. A discriminative filter bank of CSP method using a discriminative sensitive learning vector quantization (DFBCSP-DSLVQ) system is implemented. Four algorithms are then combined to form three methods for improving the efficiency of the DFBCSP-DSLVQ method, namely the kernel linear discriminant analysis (KLDA), the kernel principal component analysis (KPCA), the soft margin support vector machine (SSVM) classifier and the generalized radial bases functions (GRBF) kernel. The GRBF is used as a kernel for the KLDA, the KPCA feature selection algorithms and the SSVM classifier. In addition, three types of classifiers, namely K-nearest neighbor (K-NN), neural network (NN) and traditional support vector machine (SVM), are employed to evaluate the efficiency of the classifiers. Results show that the best algorithm is the combination of the DFBCSP-DSLVQ method using the SSVM classifier with GRBF kernel (SSVM-GRBF), in which the best average accuracy, attained are 92.70% and 83.21%, respectively. Results of the Repeated Measures ANOVA shows the statistically significant dominance of this method atp< 0.05. The presented algorithms are then compared with the base algorithm of this study i.e. the DFBCSP-DSLVQ with the SVM-RBF classifier. It is concluded that the algorithms, which are based on the SSVM-GRBF classifier and the KLDA with the SSVM-GRBF classifiers give sufficient accuracy and reliable results.

This paper investigates a method for imaginary hand fisting pattern recognition based on the electroencephalography (EEG). The proposed method estimate the largest Lyapunov exponent (LLE) chaotic feature that is based on approximation of mutual information (MI) and false nearest neighbor (FNN) methods for reconstructing a phase space. The selected method for MI and FNN approximation approaches is a new version of Tug of War optimization algorithm. The new algorithm utilizes chaotic maps to update candidate solutions. The chaotic approximation of the LLE (CALLE) is the utilized method for extracting the chaotic features and then categorizing features by means of soft margin support vector machine with a generalized radial basis function kernel classifier. Accuracy and paired t-test values are obtained and compared with the traditional LLE method; 18 candidates were participated to record the EEG for imaginary right-hand fisting task. The results show improvements for the CALLE algorithm in comparison with the traditional LLE by achieving a higher accuracy of 68.25%. Feature changes between two imaginary statuses were significant for 17 subjects, and the paired t-test values were (p <; 0.05). From the results, it is concluded that the Tug of War optimization method finds different values to reach a higher accuracy than the traditional LLE method, and the traditional methods for the LLE are not optimum.

Heavy equipment operation is a responsible and difficult task causing mental workload on a human operator and exposing the operator to a range of harmful factors. Human factors and ergonomics in heavy equipment design have traditionally been focused on anthropometry and questionnaires. More advanced techniques involving biosignal measurements were not applied to heavy equipment, mainly due to the diversity of real working conditions that were hard to reproduce in a laboratory environment and that prevented ambulatory studies. Recent advances in wearable biosensors and real-time simulators produce the capability of using biosignals for improving the ergonomics of heavy equipment operation. The present paper reviews the use of biosignals in human factors and the ergonomics of heavy machines by focusing on stress detection for the last ten years. The aim of the paper is analyzing the previous implemented algorithms to find a set of biosignals and methods of stress identification that could be suitable for identifying stress in heavy equipment operators both in laboratory and ambulatory studies. The conclusion emphasizes successful stress identification methods and a combination of the algorithms from different studies that facilitate the use of heavy equipment operator's applications. Also, feasible methods and directions for future research are considered.

One critical issue in brain computer interface (BCI) studies is to extract imaginary movement patterns from electroencephalograph (EEG). In this study, two different techniques —detrended fluctuation analysis (DFA) and discrete wavelet packet transform— are combined (DWPT-DFA) for feature extraction. Both approaches are known as self-similarity quantifier techniques. In wavelet technique, mother wavelets play an important role. Herein, A customized mother wavelet utilizing event related desynchronization (ERD) potential patterns are extracted and updated automatically for individual subjects. Also, three predefined mother wavelets are used, and the results are compared with the customized mother wavelet. The predefined mother wavelets are db4, db8 and coiflet 4. The soft margin support vector machine with the generalized radial basis function (SSVM-GRBF) is employed to classify the DWPT-DFA features. For the efficiency of the method, nine subjects have participated to record EEG based on the imaginary hand movements. The ERDs and features are extracted from FC1 and CP6 channels. Results show that the combination of the DWPT and DFA with the personalize ERD mother wavelet gives the best accuracy of 85.33% with p < 0.001. Based on the results, we conclude that the DWPT-DFA method using the ERD mother wavelets improves significantly the efficiency of the SSVM-GRBF classifier.