Ten-dimensional anthropomorphic arm control in a human brain−machine interface: difficulties, solutions, and limitations

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Electrocorticographic signals enable brain–computer communication in a paralyzed stroke patient.

Defining brain–machine interface applications by matching interface performance with device requirements

High-performance neuroprosthetic control by an individual with tetraplegia

Here we report that the modulation of alpha activity by covert attention can be used as a control signal in an online brain-computer interface, that it is reliable, and that it is robust. Subjects were instructed to orient covert visual attention to the left or right hemifield. We decoded the direction of attention from the magnetoencephalogram by a template matching classifier and provided the classification outcome to the subject in real-time using a novel graphical user interface. Training data for the templates were obtained from a Posner-cueing task conducted just before the BCI task. Eleven subjects participated in four sessions each. Eight of the subjects achieved classification rates significantly above chance level. Subjects were able to significantly increase their performance from the first to the second session. Individual patterns of posterior alpha power remained stable throughout the four sessions and did not change with increased performance. We conclude that posterior alpha power can successfully be used as a control signal in brain-computer interfaces. We also discuss several ideas for further improving the setup and propose future research based on solid hypotheses about behavioral consequences of modulating neuronal oscillations by brain computer interfacing.

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.

A brain-computer interface (BCI) is a new communication channel between the human brain and a digital computer. Furthermore a BCI enables communication without using any muscle activity for a subject. The ambitious goal of a BCI is finally the restoration of movements, communication and environmental control for handicapped people. However, in more recent research also BCI control in combination with Virtual Environments (VE) gains more and more interest. Within this study we present experiments combining BCI systems and control VE for navigation and control purposes just by thoughts. A comparison of the applicability and reliability of different BCI types based on event related potentials (P300 approach) will be presented.BCI experiments for navigation in VR were conducted so far with (i) synchronous BCI and (ii) asynchronous BCI systems. A synchronous BCI analyzes the EEG patterns in a predefined time window and has 2–3 degrees of freedom. A asynchronous BCI analyzes the EEG signal continuously and if a specific event is detected then a control signal is generated. This study is focused on a BCI system that can be realized for Virtual Reality (VR) control with a high degree of freedom and high information transfer rate. Therefore a P300 based human computer interface has been developed in a VR implementation of a smart home for controlling. the environment (television, music, telephone calls) and navigation control in the house.Results show that the new P300 based BCI system allows a very reliable control of the VR system. Of special importance is the possibility to select very rapidly the specific command out of many different choices. This eliminates the usage of decision trees as previously done with BCI systems.KeywordsBrain-Computer InterfaceP300evoked potentialVirtual Environment

A Brain Computer Interface (BCI) is a communication system that provides an alternative channel to natural communication and control processes since it bypasses the body's normal efferent pathways, which are the neuromuscular output channels.

The objective of the study is to develop a prosthetic hand for the usage of Brain-computer Interface (BCI) system. In the proposed BCI system the prosthetic hand was introduced as an external device controlled by the system. This hand is required to perform four essential tasks of the human hand: cylindrical grasp, key pinch, pulp to pulp pinch and tripod pinch. The hand was inspired by the perfection and complexity of the human hand. This hand consists of palm and 5 fingers with a total of 16 degrees of freedom (DOF). The phalanges of each finger was modeled as three link open chain joined at the metacarpal joint (MCP), proximal joint (PIP) and distal joint (DIP). Phalanx was made from two identical parallel aluminum plates and connected to the other segment using a bolted spacer acting as hinge joint. The Length of each segment was made such that it will form an equiangular motion path during trajectory. Each joint is actuated by its individual actuator. Two mechanisms were proposed in this study. The first mechanism is the tendon drive; used terelyne string to pull each segment to flexion. The second mechanism is a spring return; a stored resistive force in torsion spring will kick the segment to its initial position. The hand was equipped with potentiometers and force sensors for control purposes. The prototype of the prosthetic hand was tested with BCI system, in order to meet its initial objective and additional tests were carried out to evaluate its performance. An experiment to test the performance of the prosthetic hand was carried out successfully. Strength of each tendon was measured using a proof ring method and motion images were captured using video camera and analyzed using Peak Motus 7 Motion Analysis software.

Attention modulations of posterior alpha as a control signal for two-dimensional brain–computer interfaces

This paper proposes a novel method of using electromyographic (EMG) potentials generated by the forearm muscles during hand and finger movements to control an artificial prosthetic hand worn by an amputee. Surface EMG sensors were used to record a sequence of forearm EMG potential signals via a PC sound card and a novel 3-D electromagnetic positioning system together with a data-glove mounted with 11 miniature electromagnetic sensors used to acquire corresponding human hand pose in real time. The synchronized measurements of hand posture and associated EMG signals stored as prototypes embody a numerical expression of the current hand shape in the form of a series of data frames, each comprising a set of postures and associated EMG data. This allows a computer generated graphical 3-D model, combined with synthesized EMG signals, to be used to evaluate the approach. This graphical user interface could also enable handicapped users to practice controlling a robotic prosthetic hand using EMG signals derived from their forearm muscles. We believe this task might be made easier using a dictionary of stored task-specific prototype data frames acquired from able-bodied users. By comparing the resulting EMG data frames with stored prototypes, the most likely data frame sequence can be identified and used to control a robotic hand so that it carries out the user's desire. We explore the feasibility of this approach by applying frequency analysis on the signal derived from a multichannel EMG measurement device and identify pattern recognition techniques in the time and frequency domains to determine plausible hand shapes. This approach offers several advantages over existing methods. First, it simplifies the classification procedure, saving computational time and the requirement for the optimization process, and second, it increases the number of recognizable hand shapes, which in turn improves the dexterity of the prosthetic hand and the quality of life for amputees. The database of EMG prototypes could be employed to optimize the accuracy of the system within a machine learning paradigm. By making a range of EMG prototype databases available, prosthetic hand users could train themselves to use their prosthesis using the visual reference afforded by the virtual hand model to provide feedback

Abstract: Brain-computer interface (BCI) technology has emerged as a groundbreaking innovation with transformative potential in medical devices. BCIs are analyzed for their ability to diagnose, treat, and manage neurological disorders, such as Parkinson's disease, ALS, and stroke. The study explores the integration of BCI technology into medical devices and examines the challenges and opportunities regulatory authorities face in overseeing this rapidly evolving field. The study employs a comprehensive literature review with the help of databases like Google Scholar, and PubMed, analyzing case studies and regulatory requirements. BCI technology enables direct communication between the human brain and external devices, allowing for the control of computers or prosthetic limbs. Additionally, software tools facilitate the analysis of recorded brain signals, aided by advancements in Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), for automatic classification of EEG signals. However, the rapid advancement leads to high costs and complexity of BCI systems which can limit their accessibility and scalability, posing a barrier. Moreover, the development of standardized protocols and guidelines for BCI implementation is essential to maintain consistency and reliability across applications. The ethical considerations surrounding BCI technology are vital and emphasize the need for government regulations to ensure its safe and effective integration into healthcare. BCI's potential for responsible innovation in patient-centric care is advocated, propelling medical technology into a new era of seamless integration and improved patient outcomes.

A brain computer interface (BCI) is a real-time communication system designed to allow users to voluntarily send messages or commands without sending them through the brain's normal output pathways. We designed a real-time BCI Auxiliary game platform for motion disabled. A system that uses the human ability to control a portable video game device through some electroencephalographic (EEG) rhythms has also been presented. The neural signals obtained by EEG recording equipment are sent through a Web Service technology connection to a PC that processes it in real time and sends it wirelessly to a mobile gaming device. Web Service has been used as a wireless technology for sending EEG signals to a PC and vice versa, the PDA gets the command from the PC though the Web Service technology, which has been proposed and furthers it showing that the system is reliable and robust enough to work in BCI systems. In this study we have investigated the human’s ability to play a video game by manipulating neuronal motor cortex activity in the presence of a visual feedback environment. This paper presents one of these solutions, which control games by BCI based on motor imagery. This type of BCI solution is translating the motor imagery EEG into three control signals: left, right or transmutation. And then use it as an input device of a simple game player to realize the function of operating games. The system digitizes and stores the EEG signal, and exports the results of Signal Analysis on the Monitors to achieve the basic function which is playing pc-game through the human’s ideas. COM technology allows the BCI platform has strong scalability to meet the needs of various types of brain computer interface solution for the games. This paper presents one of these solutions, which control games by BCI based on motor imagery. This type of BCI solution is translating the motor imagery EEG into three control signals: left, right or stop. And then use it as an input device of a simple game player to realize the function of operating games.

Inside the Mind's Eye: An International Perspective on Data Privacy Law in the Age of Brain-Machine Interfaces

BackgroundMost partial hand amputees experience limited wrist movement. The limited rotational wrist movement deteriorates natural upper limb system related to hand use and the usability of the prosthetic hand, which may cause secondary damage to the musculoskeletal system due to overuse of the upper limb affected by repetitive compensatory movement patterns. Nevertheless, partial hand prosthetics, in common, have only been proposed without rotational wrist movement because patients have various hand shapes, and a prosthetic hand should be attached to a narrow space.MethodsWe hypothesized that partial hand amputees, when using a prosthetic hand with a wrist rotation module, would achieve natural upper limb movement muscle synergy and motion analysis comparable to a control group. To validate the proposed prototype design with the wrist rotation module and verify our hypothesis, we compared a control group with partial hand amputees wearing hand prostheses, both with and without the wrist rotation module prototype. The study contained muscle synergy analysis through non-negative matrix factorization (NMF) using surface electromyography (sEMG) and motion analyses employing a motion capture system during the reach-to-grasp task. Additionally, we assessed the usability of the prototype design for partial hand amputees using the Jebsen-Taylor hand function test (JHFT).ResultsThe results showed that the number of muscle synergies identified through NMF remained consistent at 3 for both the control group and amputees using a hand prosthesis with a wrist rotation module. In the motion analysis, a statistically significant difference was observed between the control group and the prosthetic hand without the wrist rotation module, indicating the presence of compensatory movements when utilizing a prosthetic hand lacking this module. Furthermore, among the amputees, the JHFT demonstrated a greater improvement in total score when using the prosthetic hand equipped with a wrist rotation module compared to the prosthetic hand without this module.ConclusionIn conclusion, integrating a wrist rotation module in prosthetic hand designs for partial hand amputees restores natural upper limb movement patterns, reduces compensatory movements, and prevent the secondary musculoskeletal. This highlights the importance of this module in enhancing overall functionality and quality of life.