A comprehensive review of EMG/EEG based wheelchair control systems for individuals with disabilities: HMI and BCI perspectives.

A brain computer interface (BCI) is a communication system converting neural activities into signals that can control computer cursors or external devices (Fetz, 2007). BCI was initially and mainly employed for patients with severe motor disorders such as amyotrophic lateral sclerosis (ALS) by providing non-muscular bidirectional communication and control. How‐ ever, the application of BCI has been extended to control various EEG signals for therapeutic purposes, such as seizure control in epilepsy patients. Although such BCIs did not demonstrate rapid control as in non-muscular communication, it still assumes that EEG based bidirectional control is possible (Wolpaw et al., 2002). More specifically, a BCI in epilepsy research, as in the current chapter, refers to a communication system capable to acquire signal and to implement real-time seizure detection/prediction and contingent delivery of warning stimuli or therapies such as electrical stimulation to control seizures (see the diagram). Such systems became feasible with technological development, and have been implemented in animal and human to control seizures. In the current chapter we will first give an overview of application of BCI, especially with deep brain stimulation in epilepsy research. Then we will discuss different components of a BCI system: input (signal acquisition), algorithm (seizure detection/predic‐ tion) and output (application and users), in particular stressing some important issues on BCI performance.

This paper presents a new shared-control approach for assistive mobile robots, using Brain Computer Interface (BCI) as the Human-Machine Interface (HMI). A P300-based paradigm that allows the selection of brain-actuated commands to steer a Robotic Wheelchair (RW), is proposed. At least one specific motor skill, such as the control of arms, legs, head or voice, is required to operate a conventional HMI. Due to this reason, they are not suited for people suffering from severe motor disorders. BCI may open a new communication channel to these users, since it does not require any muscular activity. The number of decoded symbols per minute (SPM) in a BCI is still very low, which means that users can only provide sparse, and discrete commands. The RW must rely on the navigation system to validate user commands effectively. A two-layer shared-control approach is proposed. The first, a virtual-constraint layer, is responsible for enabling/disabling the user commands, based on certain context restrictions. The second layer is an user-intent matching responsible for determining the suitable steering command, that better fits the user command, taking the user competence on steering the wheelchair into account. Experimental results using Robchair, the RW platform developed at ISR-UC [1], [2] are presented, showing the effectiveness of the proposed methodologies.

Background - Brain-Computer Interfaces (BCI) enable their users to interact and communicate with the environment without requiring intact muscle control. To this end, brain activity is directly measured, digitized and interpreted by the computer. Thus, BCIs may be a valuable tool to assist severely or even completely paralysed patients. Many BCIs, however, rely on neurophysiological potentials evoked by visual stimulation, which can result in usability issues among patients with impaired vision or gaze control. Because of this, several non-visual BCI paradigms have been developed. Most notably, a recent study revealed promising results from a tactile BCI for wheelchair control. In this multi-session approach, healthy participants used the BCI to navigate a simulated wheelchair through a virtual apartment, which revealed not only that the BCI could be operated highly efficiently, but also that it could be trained over five sessions. The present thesis continues the research on this paradigm in order to - confirm its previously reported high performance levels and trainability - reveal the underlying factors responsible for observed performance increases - establish its feasibility among potential impaired end-users Methods - To approach these goals, three studies were conducted with both healthy participants and patients with amyotrophic lateral sclerosis (ALS). Brain activity during BCI operation was recorded via electroencephalography (EEG) and interpreted using a machine learning-based linear classifier. Wheelchair navigation was executed according to the classification results and visualized on a monitor. For offline statistical analysis, neurophysiological features were extracted from EEG data. Subjective data on usability were collected from all participants. Two specialized experiments were conducted to identify factors for training. Results and Discussion - Healthy participants: Results revealed positive effects of training on BCI performances and their underlying neurophysiological potentials. The paradigm was confirmed to be feasible and (for a non-visual BCI) highly efficient for most participants. However, some had to be excluded from analysis of the training effects because they could not achieve meaningful BCI control. Increased somatosensory sensitivity was identified as a possible mediator for training-related performance improvements. Participants with ALS: Out of seven patients with various stages of ALS, five could operate the BCI with accuracies significantly above chance level. Another ALS patient in a state of near-complete paralysis trained with the BCI for several months. Although no effects of training were observed, he was consistently able to operate the system above chance level. Subjective data regarding workload, satisfaction and other parameters were reported. Significance - The tactile BCI was evaluated on the example of wheelchair control. In the future, it could help impaired patients to regain some lost mobility and self-sufficiency. Further, it has the potential to be adapted to other purposes, including communication. Once visual BCIs and other assistive technologies fail for patients with (progressive) motor impairments, vision-independent paradigms such as the tactile BCI may be among the last remaining alternatives to interact with the environment. The present thesis has strongly confirmed the general feasibility of the tactile paradigm for healthy participants and provides first clues about the underlying factors of training. More importantly, the BCI was established among potential end-users with ALS, providing essential external validity.

Brain-Computer Interface (BCI) technology may provide individuals with motor impairments or even the general population a new way to interact with the world around them. However, current BCI systems using electroencephalography (EEG) can be unreliable and produce large variations in performance. Most studies seek to improve performance by focusing on signal processing and classification techniques. However, it may also be beneficial to investigate different control strategies. For this reason, the main objective of this pilot study was to investigate the use of visual imagery, a control paradigm that has not been much tested for EEG BCI applications. Visual imagery may provide a more intuitive control strategy with a greater number of available classes than other popular imagery-based methods such as motor imagery. Using this paradigm, we have demonstrated above chance binary classification accuracy (59.9%, p < 0.05) during offline decoding of face and scene visual imagery. Furthermore, the participant in this study achieved significantly above chance performance during a three-class, closed-loop BCI interaction (47.2%, p = 0.05). The initial results of this pilot study demonstrate the feasibility of using visual imagery as an alternative EEG BCI control paradigm.

Upper limb myoelectric prosthetic control is an essential topic in the field of rehabilitation. The technique controls prostheses using surface electromyogram (sEMG) and intramuscular EMG (iEMG) signals. EMG signals are extensively used in controlling prosthetic upper and lower limbs, virtual reality entertainment, and human-machine interface (HMI). EMG signals are vital parameters for machine learning and deep learning algorithms and help to give an insight into the human brain's function and mechanisms. Pattern recognition techniques pertaining to support vector machine (SVM), k-nearest neighbor (KNN) and Bayesian classifiers have been utilized to classify EMG signals. This paper presents a review on current EMG signal techniques, including electrode array utilization, signal acquisition, signal preprocessing and post-processing, feature selection and extraction, data dimensionality reduction, classification, and ultimate application to the community. The paper also discusses using alternatives to EMG signals, such as force sensors, to measure muscle activity with reliable results. Future implications for EMG classification include employing deep learning techniques such as artificial neural networks (ANN) and recurrent neural networks (RNN) for achieving robust results.

Brain-Computer Interfaces (BCI) give rise to a communication means between individuals with severe motor disorders, and their external world via the measurement of the electroencephalographic (EEG) activity. BCI users may control this activity by concentrating on a specific mental task. Motor imagery (MI) executions have become the most used mental task by BCI-groups. Despite a large number of references describing the theoretical framework of MI-based BCIs, there is not enough information related to the available computer software that could be suitable to develop a specific-purpose, efficient and straightforward BCI. Therefore, the aims of this paper are: (1) to develop a MI-based BCI system making use of Python programming language, and (2) to study MI signals of three users via the proposed BCI system in order to adapt a computer for posterior applications. The use of Python along with plug-ins for developing MI-based BCI systems is not only feasible, but also it is proficient. Moreover, the Python community provides extensive variety of tools to design compelling systems.

Chapter 15 Matching Brain–Machine Interface Performance to Space Applications

This paper deals with the issue of the brain-computer interface (BCI) – the human-machine interface (HMI) based on acquisition, analysis and transformation of signals generated by the central nervous system (CNS) as the manifestation of its normal function. Brain-computer interface can be seen as the bridge that is building up direct one-way or two-way communication pathway between the brain and the external technical device. Paper introduces techniques based on non-invasive functional imaging of the brain used for data acquisition in non-invasive brain computer interfaces, and is focused on the technique that is reading neural activity of the brain with use of multi-channel electroencephalograph (EEG). As the part of this paper we are introducing our experience with the low-cost commercially available equipment Emotiv EPOC Neuroheadset based on this technology.

Brain-Computer Interface (BCI) is a young research area for researchers. Increasing number of research activities improves several areas such as signal acquisition techniques, hardware development, machine learning, and signal processing and system integration. However, there are many disadvantages of conventional BCI approaches. For example, Motor-imagery based BCIs requires extensive training of the subjects, P-300 based BCIs still requires several stimulus repetitions to obtain reliable accuracy and in SSVEP stimulus; number of commands is limited by the number of stimulus frequencies and many more. To overcome these disadvantages and further improvement in performance of the system, an increasing number of researchers have begun to explore hybrid BCI approaches, in which multiple BCI approaches are incorporated in a single BCI system. The purpose of this paper is to give a brief introduction to the different types of hybrid BCI techniques. There are many different types of hybrid BCI that can be used in a wide range of applications. The paradigm design plays a very important role in the performance of hybrid BCIs.

A review of disability EEG based wheelchair control system: Coherent taxonomy, open challenges and recommendations

A hybrid brain computer interface system based on the neurophysiological protocol and brain-actuated switch for wheelchair control

Severely disabled people, like completely paralyzed persons either with tetraplegia or similar disabilities who cannot use their arms and hands, are often considered as a user group of Brain Computer Interfaces (BCI). In order to achieve high acceptance of the BCI by this user group and their supporters, the BCI system has to be integrated into their support infrastructure. Critical disadvantages of a BCI are the time consuming preparation of the user for the electroencephalography (EEG) measurements and the low information transfer rate of EEG based BCI. These disadvantages become apparent if a BCI is used to control complex devices. In this paper, a hybrid BCI is described that enables research for a Human Machine Interface (HMI) that is optimally adapted to requirements of the user and the tasks to be carried out. The solution is based on the integration of a Steady-state visual evoked potential (SSVEP)-BCI, an Event-related (de)-synchronization (ERD/ERS)-BCI, an eye tracker, an environmental observation camera, and a new EEG head cap for wearing comfort and easy preparation. The design of the new fast multimodal BCI (called sBCI) system is described and first test results, obtained in experiments with six healthy subjects, are presented. The sBCI concept may also become useful for healthy people in cases where a “hands-free” handling of devices is necessary.

The object of research: The research object is brain-computer interfaces (BCI) and issues related to the acquisition, processing, and analysis of neural signals. Investigated problem: The research focuses on the challenges related to the acquisition and processing of neural signals in brain-computer interface (BCI) systems and the solutions required to improve the system's efficiency. Specifically, issues such as signal weakness, data loss, artifacts, difficulties in real-time operation, and individual adaptation requirements are emphasized. Additionally, the comparison of invasive and non-invasive BCIs, the advantages and limitations of both approaches, and cybersecurity risks are central topics of this study. The goal is to overcome these challenges and develop new signal processing techniques and artificial intelligence algorithms to make BCI technologies more accurate, faster, and reliable. The main scientific results: The factors determining the effectiveness of BCI systems: actors such as the acquisition and processing of neural signals, the algorithms used for signal analysis, hardware, and user feedback are identified as key elements affecting the performance of BCI systems. Comparison of invasive and non-invasive BCIs: Both approaches' advantages and limitations have been reviewed. Invasive BCIs allow for more accurate signal acquisition but require surgical intervention. Non-invasive BCIs, on the other hand, are more comfortable and safer but are prone to artifacts and data loss. Advancements in signal processing methods: The application of new signal processing techniques and artificial intelligence algorithms is emphasized as crucial to improving the efficiency of BCI systems. Individual adaptation and real-time operation challenges: BCI systems' need for individual adaptation and the challenges of real-time operation are highlighted as significant problems that negatively affect system efficiency. Cybersecurity risks: There are cybersecurity risks associated with the remote control of BCIs, which pose a serious threat, particularly for medical implants and neurological devices. Improved signal analysis algorithms: The importance of algorithms, particularly approaches like SVM and LDA, for the classification of motor imagery signals and the correct analysis of signals is emphasized. The area of practical use of the research results: BCI systems can be used in the rehabilitation process for individuals suffering from neurological diseases or physical disabilities. These systems can help restore patients' physical and neural functions. BCI technologies can be applied in controlling robots, especially robotic prosthetics and interactions with the environment for individuals with disabilities. BCIs could allow users to control computers and other technological devices through thought, enabling more natural and comfortable interactions with technology. The use of EEG signals in biometrics could provide a novel approach for identifying individuals and ensuring data security. Innovative technological product: The innovative technological product is BCI systems. This technology connects brain activity directly with computers or other devices, enabling various applications. Specifically, BCIs open new possibilities in medical rehabilitation, robotics, human-computer interaction, security, and biometric identification. The article highlights the importance of applying artificial intelligence algorithms and new signal processing techniques to improve the efficiency of BCI systems. This aims to ensure that BCI systems operate more accurately, quickly, and reliably. Such technologies can lead to significant advancements, particularly in the fields of medical devices and robotics. Scope of the innovative technological product: BCI systems are widely used across various fields. In the medical sector, particularly in neurorehabilitation, they are extensively applied. Brain signals are utilized in the treatment of several neurological disorders. Additionally, in robotics, the development of brain-controlled robotic arms, exoskeletons, and autonomous systems that enhance human capabilities – especially for individuals with limited mobility – is closely linked to the integration of this technology into automation. BCI systems are also successfully implemented in Human-Computer Interaction (HCI), the education sector, and security fields. The uniqueness of brainwave patterns makes BCIs a promising tool for biometric authentication. Unlike traditional security methods, brain-based authentication systems offer a higher level of security. In the modern era, advancements in artificial intelligence, machine learning, and signal processing are making BCIs more efficient and accessible, enabling their broad integration into various aspects of daily life

Brain-computer interfaces (BCIs) can provide a means of communication to individuals with severe motor disorders, such as those presenting as locked-in. Many BCI paradigms rely on motor neural pathways, which are often impaired in these individuals. However, recent findings suggest that visuospatial function may remain intact. This study aimed to determine whether visuospatial imagery, a previously unexplored task, could be used to signify intent in an online electroencephalography (EEG)-based BCI. Eighteen typically developed participants imagined checkerboard arrow stimuli in four quadrants of the visual field in 5-s trials, while signals were collected using 16 dry electrodes over the visual cortex. In online blocks, participants received graded visual feedback based on their performance. An initial BCI pipeline (visuospatial imagery classifier I) attained a mean accuracy of [Formula: see text]% classifying rest against visuospatial imagery in online trials. This BCI pipeline was further improved using restriction to alpha band features (visuospatial imagery classifier II), resulting in a mean pseudo-online accuracy of [Formula: see text]%. Accuracies exceeded the threshold for practical BCIs in 12 participants. This study supports the use of visuospatial imagery as a real-time, binary EEG-BCI control paradigm.

The human forearm is one of the most densely distributed parts of the human body, with the most irregular spatial distribution of muscles. A number of specific forearm muscles control hand motions. Acquiring high-fidelity sEMG signals from human forearm muscles is vital for human-machine interface (HMI) applications based on gesture recognition. Currently, the most commonly used commercial electrodes for detecting sEMG or other electrophysiological signals have a rigid nature without stretchability and cannot maintain conformal contact with the human skin during deformation, and the adhesive hydrogel used in them to reduce skin-electrode impedance may shrink and cause skin inflammation after long-term use. Therefore, developing elastic electrodes with stretchability and biocompatibility for sEMG signal recording is essential for developing HMI. Here, we fabricated a nanocomposite hybrid on-skin electrode by infiltrating silver nanowires (AgNWs), a one-dimensional (1D) nano metal material with conductivity, into polydimethylsiloxane (PDMS), a silicone elastomer with a similar Young's modulus to that of the human skin. The AgNW on-skin electrode has a thickness of 300 μm and low sheet resistance of 0.481 ± 0.014 Ω/sq and can withstand the mechanical strain of up to 54% and maintain a sheet resistance lower than 1 Ω/sq after 1000 dynamic strain cycles. The AgNW on-skin electrode can record high signal-to-noise ratio (SNR) sEMG signals from forearm muscles and can reflect various force levels of muscles by sEMG signals. Besides, four typical hand gestures were recognized by the multichannel AgNW on-skin electrodes with a recognition accuracy of 92.3% using machine learning method. The AgNW on-skin electrode proposed in this study has great potential and promise in various HMI applications that employ sEMG signals as control signals.