Argumentaire

Assistive Device With Conventional, Alternative, and Brain-Computer Interface Inputs to Enhance Interaction With the Environment for People With Amyotrophic Lateral Sclerosis: A Feasibility and Usability Study

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.

Individuals suffering from a progressive neurodegenerative disease, such as amyotrophic lateral sclerosis (ALS), will lose muscle function over time and become completely paralysed. For some time, people with ALS may retain functional tongue movement, despite losing mobility below the neck. These individuals can benefit from using an inductive tongue control interface (ITCI) to control computers or assistive robotic devices to gain independence in their daily lives. Eventually, when the individual can no longer use their tongue, they can rely on a brain computer interface (BCI). However, these require a lot of data to calibrate and function properly. Recording this data while the individual can still use the ITCI can potentially speed up the training process, allowing for an easier transition between interface technologies. This study investigates whether it is possible to create a background data collector for a BCI based on attempted tongue movement by analyzing brain patterns of two individuals with ALS while using an ITCI. The participants used an inductive ITCI in simple cued movement trials while electroencephalogram (EEG) was collected from the motor cortex. The EEG signal indicated that movement-related cortical potentials (MRCP) were generated after the cued movements. After synchronising the signal to the activations recorded on the ITCI, the MRCP became even more apparent. Therefore, it is concluded that it is possible to record MRCPs from individuals with ALS performing tongue movements, that the ITCI can assist in better extracting synchronized MRCP epochs, and that a background data collector for a tongue movement intention-based BCI is very feasible.

Objective. Brain–machine interfaces (BMI) are an emerging therapeutic option that can allow paralyzed patients to gain control over assistive technology devices (ATDs). BMI approaches can be broadly classified into invasive (based on intracranially implanted electrodes) and noninvasive (based on skin electrodes or extracorporeal sensors). Invasive BMIs have a favorable signal-to-noise ratio, and thus allow for the extraction of more information than noninvasive BMIs, but they are also associated with the risks related to neurosurgical device implantation. Current noninvasive BMI approaches are typically concerned, among other issues, with long setup times and/or intensive training. Recent studies have investigated the attitudes of paralyzed patients eligible for BMIs, particularly patients affected by amyotrophic lateral sclerosis (ALS). These studies indicate that paralyzed patients are indeed interested in BMIs. Little is known, however, about the degree of knowledge among paralyzed patients concerning BMI approaches or about how patients retrieve information on ATDs. Furthermore, it is not yet clear if paralyzed patients would accept intracranial implantation of BMI electrodes with the premise of decoding improvements, and what the attitudes of a broader range of patients with diseases such as stroke or spinal cord injury are towards this new kind of treatment. Approach. Using a questionnaire, we surveyed 131 paralyzed patients for their opinions on invasive BMIs and their attitude toward invasive BMI treatment options. Main results. The majority of the patients knew about and had a positive attitude toward invasive BMI approaches. The group of ALS patients was especially open to the concept of BMIs. The acceptance of invasive BMI technology depended on the improvements expected from the technology. Furthermore, the survey revealed that for paralyzed patients, the Internet is an important source of information on ATDs. Significance. Websites tailored to prospective BMI users should be further developed to provide reliable information to patients, and also to help to link prospective BMI users with researchers involved in the development of BMI technology.

Brain-computer interfaces (BCIs) have the potential to preserve or restore communication and device control in people with paralysis from a variety of causes. For people living with amyotrophic lateral sclerosis (ALS), however, the progressive loss of cortical motor neurons could theoretically pose a challenge to the stability of BCI performance. Here we tested the stability of gesture decoding with a chronic electrocorticographic (ECoG) BCI in a man living with ALS and participating in a clinical trial (ClinicalTrials.gov, NCT03567213). We evaluated offline decoding performance of attempted gestures over two periods: a 5-week period beginning roughly 2 years post-implant and a 6-week period ending roughly 5 months later. Decoder sensitivity was high in both periods (90 – 98%), while classification accuracy was 37 – 68% in the first period and worsened to 23 – 39% in the second. We investigated multiple frequency bands that were used as model features in both periods, and we observed reductions in high gamma band power (70 – 110 Hz) and between-class separation during the second period compared to the first. Over the 5-month period motor function did not appreciably decline. These results, albeit preliminary, suggest that declines in the neural population responses that drive ECoG BCI performance can occur without overt signs of disease progression in people living with ALS, and could serve as a biomarker for disease progression in the future.

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

Speech is essential for human communication, but millions of people lose the ability to speak due to conditions such as amyotrophic lateral sclerosis (ALS) or stroke. Assistive technologies like brain-computer interfaces (BCIs), can convert brain signals into speech. However, these technologies still face challenges in decoding accuracy. This issue is especially challenging for tonal languages like Mandarin Chinese. Furthermore, most existing speech datasets are based on Indo-European languages, which hinders our understanding of how tonal information is encoded in the brain. To address this, we introduce a comprehensive open dataset, which includes multimodal signals from 30 subjects using Mandarin Chinese across overt, silent, and imagined speech modes, covering electroencephalogram (EEG), surface electromyogram (sEMG), and speech recordings. This dataset lays a valuable groundwork for exploring the neural encoding of tonal languages, investigating tone-related brain dynamics, and improving assistive communication strategies. It supports cross-linguistic speech processing research and contributes to data-driven neural speech decoding technology innovations.

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.

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.

It is impossible for severely disabled people to browse or learn through the Internet due to the mere lack of independent control of the mouse. This paper proposes a brain computer interface (BCI) to aid severely disabled individuals, such as people disabled by amyotrophic lateral sclerosis (ALS), in browsing or learning on the Internet. By analyzing specific components of event-related potentials (ERPs), cursor control can be achieved. The cursor can be controlled by brain waves in a user-friendly manner to move or click on the web page. The major contributions of this research include: (1) designing a BCI for disabled people, (2) embedding the BCI's cursor controller into a web page, (3) conducting clinical experiments, (4) analysing the experimental results, system accuracy, and effectiveness, and (5) evaluating the system's practicability and giving suggestions for future work. There are two innovative technologies proposed in this research: (1) a specific component of ERPs located at O1, the N2P3 (the difference between the peak and trough of N200 and P300), was used to differentiate targeted from non-targeted (non-selected) signals, (2) instead of a fixed position style BCI, a movable watermark style BCI which follows the cursor was designed. The novel technique supported by a user-friendly interface helps the disabled have contact with the outside world resulting in positive emotions.

This chapter provides an overview of studies that have evaluated the use of assistive technology to improve the communication functioning of individuals with specific types of communication disorders. The studies are divided into three groups based on the nature of the presenting communication disorder. The first group concerns studies that targeted individuals with cerebral palsy. The second group concerns studies on using assistive technology for people with other motor speech disorders, such as apraxia, dysarthria, and amyotrophic lateral sclerosis (ALS). The third group consists of studies focused on the use of assistive communication technologies to improve spoken and written language expression in people with aphasia. The final part of this chapter analyzes the overall outcomes of the three groups of studies reviewed, considers the implications of these findings for overall communication and social functioning, and highlights directions for future research.

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is a neurological disease that occurs as a result of damage to the nerves in the brain and restriction of muscle movements. Electroencephalography (EEG) is the most common method used in brain imaging to study neurological disorders. Diagnosis of neurological disorders such as ALS, Parkinson's, attention deficit hyperactivity disorder is important in biomedical studies. In recent years, deep learning (DL) models have been started to be applied in the literature for the diagnosis of these diseases. In this study, event-related potentials (ERPs) were obtained from EEG signals obtained as a result of visual stimuli from ALS patients and healthy controls. As a new method, variational mode decomposition (VMD) is applied to the produced ERP signals and the signals are decomposed into subbands. In addition, empirical mode decomposition (EMD), one of the popular decomposition methods in the literature, was also analyzed, and ERP signals were divided into subbands and compared with the VMD method. Subband signals were classified in two stages with the one-dimensional convolutional neural network (1D CNN) model, which is one of the DL techniques proposed in the study. Accuracy, sensitivity, specificity, and F1-Score measurements were obtained using 5- and 10-fold cross-validation to evaluate classifier performance. In the first stage of classification, only VMD and EMD subband signals were used and 92.95% classification accuracy was obtained by the VMD method. In the second stage, VMD, EMD subband signals, and original ERP signals were all classified together with the VMD+ERP model achieving the maximum classification accuracy rate of 90.42%. It is thought that the results of the study will contribute to the diagnosis of similar neurological disorders such as ALS, attention studies based on visual stimuli, and the development of brain-computer interface (BCI) systems using the method applied to the proposed ERP signals.

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.

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

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