An Implementation of Electroencephalogram Signals Acquisition to Control Manipulator through Brain Computer Interface

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that causes progressive loss of voluntary movement, including the ability to speak. As the disease rapidly progresses, there is a need for augmented and alternative communication that removes physical barriers required for engagement. Brain-Computer Interfaces (BCI) are a technology that allow for communication using only brain signals in response to visual stimuli. This project aimed to design an accessible and practical BCI for continuous, at-home use, and to increase BCI usability by using familiar facial stimuli to elicit a stronger brain response. The overall goal for enhancement of BCI usability is to maximize ease of use for individuals with ALS and their caregivers so that BCI technologies can make a useful impact and improve the everyday lives of people with ALS. In this project, a new miniaturized and low-cost P300 BCI system was designed using wireless and battery-operated EEG neuroimaging, modular software, and the use of an individualized BCI presentation stimuli. A 3-dimensional virtual environment platform was integrated as a navigational control output mechanism that executes commands received from the BCI to move a user's avatar in a virtual maze. Together, these form the virtual environment BCI (veBCI). The BCI stimuli presentation consisted of a custom approach of integrating different human face images with the aim of increasing user engagement and elicited brain activity, and therefore improving BCI online accuracy. The impact and effectiveness of the approach was tested via a verification BCI study in which 6 different stimuli conditions were utilized: male celebrity face, female celebrity face, male stranger face, female stranger face, a family member/close friend of the participant, and blank/no face as a control. A total of 16 volunteers (average age 56.5 and eight diagnosed with ALS) used the BCI system with all 6 face conditions in ecologically valid settings. Results indicate that this new miniaturized BCI is usable for both people with ALS and healthy controls. Significant differences existed in the amplitude of the P300 response in ALS and healthy controls. Additionally, differences between male and female participants demonstrated the potential role of personalization to optimize the visually evoked potential response and further increase BCI online accuracy. In conclusion, a home-use P300 BCI system was developed with a new generation of miniaturized, low-cost, and mobile neuroimaging running on an embedded computational platform. BCI stimuli with human face inclusion could help personalization of the BCI. Furthermore, virtual environments provide unique new ways to implement BCI output beyond typical spelling tasks and communication. Future work could focus on developing dedicated online signal processing to eliminate noise and improve online accuracy during home use.

The paper provides a comprehensive exploration of brain-computer interfaces (BCIs) and their application in addressing communication and control challenges in neurological disorders like Amyotrophic Lateral Sclerosis (ALS), locked-in syndrome, Parkinson’s disease, and Spinal Cord Injury. It traces BCI advancements from foundational information theory principles to their current state. Neurological disorders severely impact communication and control abilities, with ALS causing muscle weakness and paralysis, and locked-in syndrome confining individuals within paralyzed bodies while maintaining cognitive functions. BCIs decode brain signals, enabling control of external devices like computers, offering hope for restoring communication and control in affected individuals. Employing data analysis and visualization techniques, this study evaluates BCI performance in improving communication and control across various disorders. Matplotlib generates informative graphs and performance metrics, quantifying BCI efficacy for users with differing motor impairments. The findings highlight BCI transformative potential, guiding clinicians and researchers toward personalized solutions for diverse patient populations. This research underscores the necessity for continued innovation and exploration in BCI technology, envisioning a more inclusive and adaptive future for individuals with neurological disorders

Enhancing the Social Impact of Contemporary Music with Neurotechnology

Brain-computer interfaces (BCIs) aim to help paralysed patients to interact with their environment by controlling external devices using brain activity, thereby bypassing the dysfunctional motor system. Some neuronal disorders, such as amyotrophic lateral sclerosis (ALS), severely impair the communication capacity of patients. Several invasive and non-invasive brain-computer interfaces (BCIs), most notably using electroencephalography (EEG), have been developed to provide a means of communication to paralysed patients. However, except for a few reports, all available BCI literature for the paralysed (mostly ALS patients) describes patients with intact eye movement control, i.e. patients in a locked-in state (LIS) but not a completely locked-in state (CLIS). In this article we will discuss: (1) the fundamental neuropsychological learning factors and neurophysiological factors determining BCI performance in clinical applications; (2) the difference between LIS and CLIS; (3) recent development in BCIs for communication with patients in the completely locked-in state; (4) the effect of BCI-based communication on emotional well-being and quality of life; and (5) the outlook and the methodology needed to provide a means of communication for patients who have none. Thus, we present an overview of available studies and recent results and try to anticipate future developments which may open new doors for BCI communication with the completely paralysed.

Implantable brain-computer interfaces (BCIs) are being developed to restore speech capacity for those who are unable to speak. Patients with locked-in syndrome or amyotrophic lateral sclerosis could be able to use covert speech – vividly imagining saying something without actual vocalisation – to trigger neural controlled systems capable of synthesising speech. User control has been identified as particularly pressing for this type of BCI. The incorporation of machine learning and statistical language models into the decoding process introduces a contribution to (or ‘shaping of’) the output that is beyond the user’s control. Whilst this type of ‘shared control’ of BCI action is not unique to speech BCIs, the automated shaping of what a user ‘says’ has a particularly acute ethical dimension, which may differ from parallel concerns surrounding automation in movement BCIs. This paper provides an analysis of the control afforded to the user of a speech BCI of the sort under development, as well as the relationships between accuracy, control, and the user’s ownership of the speech produced. Through comparing speech BCIs with BCIs for movement, we argue that, whilst goal selection is the more significant locus of control for the user of a movement BCI, control over process will be more significant for the user of the speech BCI. The design of the speech BCI may therefore have to trade off some possible efficiency gains afforded by automation in order to preserve sufficient guidance control necessary for users to express themselves in ways they prefer. We consider the implications for the speech BCI user’s responsibility for produced outputs and their ownership of token outputs. We argue that these are distinct assessments. Ownership of synthetic speech concerns whether the content of the output sufficiently represents the user, rather than their morally relevant, causal role in producing that output.

Brain-computer interfaces in the completely locked-in state and chronic stroke.

Objective. A brain–computer interface (BCI) allows users to control external devices using brain signals that can be recorded non-invasively via electroencephalography (EEG). Movement related cortical potentials (MRCPs) are an attractive option for BCI control since they arise naturally during movement execution and imagination, and therefore, do not require an extensive training. This study tested the feasibility of online detection of reaching and grasping using MRCPs for the application in patients suffering from amyotrophic lateral sclerosis (ALS). Approach. A BCI system was developed to trigger closing of a soft assistive glove by detecting a reaching movement. The custom-made software application included data collection, a novel method for collecting the input data for classifier training from the offline recordings based on a sliding window approach, and online control of the glove. Eight healthy subjects and two ALS patients were recruited to test the developed BCI system. They performed assessment blocks without the glove active (NG), in which the movement detection was indicated by a sound feedback, and blocks (G) in which the glove was controlled by the BCI system. The true positive rate (TPR) and the positive predictive value (PPV) were adopted as the outcome measures. Correlation analysis between forehead EEG detecting ocular artifacts and sensorimotor area EEG was conducted to confirm the validity of the results. Main results. The overall median TPR and PPV were >0.75 for online BCI control, in both healthy individuals and patients, with no significant difference across the blocks (NG versus G). Significance. The results demonstrate that cortical activity during reaching can be detected and used to control an external system with a limited amount of training data (30 trials). The developed BCI system can be used to provide grasping assistance to ALS patients.

Event Abstract Back to Event Analysis and classification of EEG signals for brain computer interfaces Ugur Halici1*, E. Agi1, C. Özgen1 and I. Ulusoy1 1 Computer Vision and Intelligent Systems Research Laboratory, Middle East Technical University, Turkey Brain Computer Interfaces (BCIs) are systems developed in order to control devices by using only brain signals. Depending on the application, the device to be controlled might be a neuroprothesis, a wheel-chair or a computer. In BCI systems, different mental activities to be performed by the users are associated with different actions on the device to be controlled. The neurophysiologic brain signals produced as results of these mental activities are collected by a data acquisition device and converted to digital form. Electroencephalogram (EEG) is the most common technique employed for data acquisition in BCI systems today, due to its time-space resolution, its price and its easiness in use when compared to the other acquisition techniques. The digital brain signals collected are processed for feature extraction and then classified in order to decide on the associated action. A control signal produced according to the decision is used to change the state of the device to be controlled. Providing a feedback signal informing the user about the new state of the controlled device completes the BCI cycle. The early BCI systems were designed to work on pre-defined neurophysiologic features, where a feedback produced by a fixed set-up was provided to the system users. In these early BCI systems, the users should learn by themselves how to produce neurophysiologic signals in order to control the system. In several studies, it is reported that weeks, even months may be needed in order the user to learn how to produce these signals by adaptation of their brain. Recently, BCI systems in which machine learning algorithms employed for adapting the BCI system by using user specific signals are started to emerge. In several studies, it is shown that the adaptation of the BCI systems in this way reduces drastically the time required the user to able to produce appropriate signals and adaptation time in terms of days, even minutes are reported [1-4] The aim of this study is to analyze EEG signals collected for BCI purposes by using techniques Principal Component Analysis, Fisher Linear Discriminant Analysis, and Independent Component Analysis in order to reduce the dimension of the feature vectors obtained by sampling EEG signals in an representative manner and then classify the low dimension feature vectors obtained in this way by using Neural Networks and Support Vector Machines, which are able to learn through samples. The experimental studies are still going on for feature extraction and classification. The experimental results obtained will be presented in the final paper for comparison purposes.

This study provides an insight into a novel steady state visually evoked potential (SSVEP) brain computer interface (BCI) approach. In this approach, four groups of light emitting diodes (LEDs) that flicker at different frequencies are used and each of these groups consist of three LEDs connected in series. By providing visual attention to these LEDs, corresponding electroencephalograph (EEG) signals were obtained in the visual cortex area of the brain. Using suitable signal processing algorithms, acquired EEG signals were classified at different frequencies and given as inputs to a brain computer interface system that can control the movement of a wheelchair. This method provides a platform for individuals who are affected by neuromuscular degenerative diseases (NMD) such as Amyotrophic Lateral Sclerosis (ALS), Locked-in Syndrome (LIS) etc, to help them lead an independent life. Two different SSVEP approaches were carried out on four healthy subjects for prototype testing. First approach was based on four groups of LEDs flickering at different frequencies ranging from 7 Hz to 15 Hz and the subjects selectively paid attention to one group of LEDs at a time. The second approach was based on simultaneous flickering of two groups of LEDs at different frequency combinations. Five trials were conducted on four subjects to test the performance of the system. The average accuracy obtained with each of the methods was greater than 70% with an average time of less than 10 seconds to trigger a command for BCI based application. The proposed system can thus provide a visual stimulator based on simple and customizable LED for a cost- effective BCI approach. Also, the efficiency and accuracy of the proposed SSVEP approach was compared to audio steady state response (ASSR) approach, where the subjects concentrated to two tones of beat frequencies at 37 Hz and 43 Hz. The average accuracy obtained with ASSR approach was only 47.5% with an average time of 18.72 seconds. Compared to ASSR, SSVEP approach is 23% more efficient.

Objective.An oculomotor brain-computer interface (BCI) records neural activity from brain regions involved in planning eye movements and translates this activity into control commands. While previous successful studies have relied on invasive implants in non-human primates or electrooculography artefacts in human electroencephalogram (EEG) data, this study aimed to demonstrate the feasibility of an oculomotor BCI using a minimally invasive endovascular StentrodeTMdevice implanted near the supplementary motor area of a patient with amyotrophic lateral sclerosis (ALS).Approach.One participant performed self-paced visually-guided and free-viewing saccade tasks in four directions (left, right, up, down) while endovascular EEG and eye gaze recordings were collected. Visually-guided saccades were cued with visual stimuli, whereas free-viewing saccades were self-directed without explicit cues. Brain signals were pre-processed to remove cardiac artefacts, downsampled, and classified using a Random Forest algorithm. For saccade onset classification (fixation vs saccade), features in time and frequency domains were extracted after xDAWN denoising, while for saccade direction classification, the downsampled time series were classified directly without explicit feature extraction.Main results.The neural responses of visually-guided saccades overlapped with cue-evoked potentials, while free-viewing saccades exhibited saccade-related potentials that began shortly before eye movement, peaked approximately 50 ms after saccade onset, and persisted for around 200 ms. In the frequency domain, these responses appeared as a low-frequency synchronisation below 15 Hz. Saccade onset classification was robust, achieving mean area under the receiver operating characteristic curve (AUC) scores of 0.88 within sessions and 0.86 across sessions. Saccade direction decoding yielded within-session AUC scores of 0.67 for four-class decoding and up to 0.75 for the best performing binary comparisons (left vs up and left vs down).Significance.This proof-of-concept study demonstrates the feasibility of an endovascular oculomotor BCI in a patient with ALS, establishing a foundation for future oculomotor BCI studies in human subjects.

The modern commercial industry is increasingly dependent on the analysis of vast data records. Analysing big electroencephalogram (EEG) signal data (called brain signal data) plays an important role in a wide variety of applications such as healthcare practices, brain computer interface (BCI) systems, innovative education, privacy and security, and biometrics. The key objective of this paper is to establish a methodological framework for identifying communicative intentions of motor disabled people from EEG data for application in BCI systems. The proposed framework is designed based on common spatial pattern (CSP) data method and optimized ensemble (OE) machine learning method for the application of BCI technologies. The CSP method is used for discovering important features from EEG data and finally the extracted features are fed as an input to optimized ensemble (OE) method. The proposed method was tested on BCI Competition III dataset IVa, which contains motor imagery-based EEG signal data. The experimental results show that our proposed method can handle brain signal big data for identifying communicative intentions for an advanced BCI system. We compared the performance of our proposed method with several other existing methods. In comparison with other established methods, our method achieves higher classification accuracy performance. This research assists the experts in processing and analysing EEG signals for BCI applications. It also supports technologists to create a new EEG data analyser for BCI systems.KeywordsElectroencephalographyBrain computer interfaceCommon spatial patternMachine learning

Neuromuscular physical therapy, also known as neurological physical therapy, focuses on assessing and treating individuals with conditions affecting the nervous system and muscles. It is crucial in promoting recovery, improving function, and enhancing the quality of life for individuals with neurological disorders. As the field continues to evolve, several new frontiers and advancements have emerged in neuromuscular physical therapy. Some of the recent trends in neuromuscular physical trends include the following. Technology-assisted rehabilitation: The integration of technology has revolutionized neuromuscular physical therapy. Various devices, such as robotic exoskeletons, virtual reality systems, and wearable sensors, are being utilized to enhance treatment outcomes. These technologies provide interactive and engaging platforms for therapy, facilitate repetitive practice, and offer real-time feedback on movement performance. They can be particularly beneficial in promoting motor recovery and functional improvements in individuals with stroke, spinal cord injury, and traumatic brain injury. Motor learning principles: There is an increased focus on incorporating motor learning principles into neuromuscular physical therapy interventions. Motor learning involves acquiring and retaining new motor skills through practice and feedback. By tailoring therapy sessions to optimize motor learning, therapists can facilitate more effective skill acquisition and long-term retention. This may involve techniques such as task-specific training, variable practice, and contextual interference. Brain-computer interfaces (BCIs): BCIs are systems that establish direct communication pathways between the brain and external devices. In the context of neuromuscular physical therapy, BCIs hold promise in neurorehabilitation. They enable individuals with severe motor impairments to control external devices or robotic exoskeletons using brain signals. BCIs can provide opportunities for functional restoration, promote neuroplasticity, and enhance the rehabilitation process for individuals with conditions like spinal cord injury or amyotrophic lateral sclerosis (ALS). Telerehabilitation: With the advancement of telehealth technology, telerehabilitation has gained momentum in neuromuscular physical therapy. Telerehabilitation allows therapists to deliver assessment and treatment remotely, overcoming barriers of geographical distance and accessibility. It enables real-time video consultations, remote exercise monitoring, education, and support. Telerehabilitation has the potential to increase access to care, improve convenience for patients, and enhance the continuity of therapy. Personalized and precision rehabilitation: The concept of personalized and precision medicine is increasingly applied to neuromuscular physical therapy. By considering individual characteristics, such as genetics, biomechanics, and neurophysiology, therapists can tailor interventions to meet specific needs and optimize outcomes. Advanced assessment tools like motion analysis systems and genetic testing can provide valuable insights for personalized treatment planning and targeted interventions. These new frontiers in neuromuscular physical therapy hold great promise in advancing the field and improving outcomes for individuals with neurological conditions. Integrating technology, motor learning principles, brain-computer interfaces, telerehabilitation, and personalized approaches can enhance the effectiveness, accessibility, and individualization of neuromuscular rehabilitation programs.

Brain-computer interfaces (BCIs) are systems which allow users to control devices, by means of their brain signals, without the involvement of the users' muscles. BCIs represent a potential solution for completely paralyzed patients who cannot communicate. We designed two new visual interfaces for controlling the movement of a virtual cursor on a monitor, implementing the cognitive principles of exogenous and endogenous attention orienting in a BCI driven by the P300. A group of patients with amyotrophic lateral sclerosis (ALS) and a matched group of healthy controls were tested. Results show that ALS patients can use both interfaces for controlling the cursor, although they reached a better performance with the endogenous attention orienting interface. We propose that the implementation of cognitive principles can play a key role in the development of new and more efficient BCIs.

During the last decade, rapid development of sophisticated methods for brain signal recordings along with availability of various efficient computational resources and the improving knowledge about brain dysfunctions have turned many researchers’ interest in using large scale neurophysiological recordings for therapeutic and replacement strategies (Mason & Birch, 2003; Millan et al., 2003). Many patients with physiological disorders such as Amyotrophic Lateral Sclerosis (ALS) or injuries such as high-level spinal cord injury suffer from disruption of the communication path between the brain and the body. People with severe motor disabilities may lose much of their voluntary muscle control. The disabled people with the above mentioned problems are forced to accept a reduced quality of life, resulting in dependence on caretakers and escalating social costs (Vaughan et al., 2003). Most of the existing assistive technology devices for these patients are not usable because these devices are dependent on motor activities from specific parts of the body. Alternative control paradigms for these individuals are thus desirable (Fatourechi, 2008). The electrophysiological signals generated from the brain can be used to command different devices, provided that the person who will control the device should also be able to control the generation of these signals. Studies showed that with sufficient training, people can control the generation of certain brain signals (Ohno et al., 2006). Having generated these signals, they can be conditioned and processed to perform the specific work for which they are generated. In other words, the interface can be made able to adapt and understand the meaning of these signals and work accordingly. If this type of Brain-Machine Interface (BMI) is successfully implemented, they can be used in developing sophisticated assistive devices (such as, a robotic wheelchair) to carry the people with motor dysfunction (Ferreira et al., 2008). Previous works in development of BMI show that the signal acquisition and processing are getting complicated with the growing availability of more sophisticated recording devices (Cheein & Postigo, 2005; Ferreira et al., 2008; Moon et al., 2005; Mourino, 2003; Rani & Sarkar, 2005). To overcome these complexities rather simple method is required to couple easily recordable neuronal signals with the robotic device (Mahmud et al., 2010; 2009). This chapter illustrates a simple BMI system using EEG signals recorded through conventional EEG 6

This chapter indicates the overview of Brain-Machine Interfaces (BMIs); the aspects of BMIs; BMIs, human-machine interfaces, and electrooculography interfaces; BMIs, Amyotrophic Lateral Sclerosis (ALS), and stroke motor recovery; speech BMIs; BMIs and neuroplasticity; and BMIs and transcranial doppler (TCD). BMIs are the computerized approaches to gaining the brain signals, investigating them, and translating them into computerized functions in order to organize the required practices. BMIs can allow people to manipulate computerized networks and various electrical devices. With the support of modern technologies, BMIs are functional and able to operate in operational settings. The chapter argues that applying BMIs has the potential to increase organizational performance and reach strategic goals in the digital age.