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

Argumentaire

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.

Cortico-efferent tract involvement in primary lateral sclerosis and amyotrophic lateral sclerosis: A two-centre tract of interest-based DTI analysis

ObjectiveIn 2017, the diagnostic criteria for cognitive and behavioural impairment in amyotrophic lateral sclerosis (ALS) with frontotemporal dementia (ALSFTD-1) have been modified (ALSFTD-2) with the inclusion of a novel category...

Management of amyotrophic lateral sclerosis in clinical practice: Results of the expert consensus using the Delphi methodology

To assess the effect of eligibility criteria in amyotrophic lateral sclerosis (ALS) clinical trials on the representativeness of the enrolled population. Patients enrolled in 8 placebo-controlled clinical trials in our ALS center from 2003 to 2008 were compared 1) to the patients included a prospective epidemiologic register (Piemonte and Valle d'Aosta register for ALS, PARALS) in the same period and 2) the subset of PARALS patients who met the usual criteria for inclusion in clinical trials (PARALS-ct) (definite, probable, probable laboratory-supported ALS; age between 18 and 75 years; disease duration <36 months; vital capacity at diagnosis ≥70%; score ≥3 at the items swallowing and respiratory insufficiency at the Amyotrophic Lateral Sclerosis Functional Rating Scale-revised scale; riluzole therapy). A total of 164 patients were enrolled in 8 different clinical trials. The PARALS cohort included 813 patients, of whom 539 (66.3%) met the entry criteria for clinical trials. Patients enrolled in clinical trials were different from both epidemiologic cohorts, since they were younger, had a longer diagnostic delay, and were more likely to have a spinal onset, and to be men. Tracheostomy-free survival was significantly longer in the group of patients enrolled in clinical trials (median survival time, trial patients, 3.9 years [95% confidence interval (CI) 3.4-4.4]; PARALS, 2.6 [2.4-2.8]; PARALS-ct, 2.9 [2.7-3.1]). Patients enrolled in clinical trials do not satisfactorily represent the ALS population; consequently, the findings of ALS trials lack of external validity (generalizability). Efforts should be made to improve patients' recruitment in trials, particularly enrolling incident rather than prevalent cases.

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.

ObjectivesTo describe the natural history and clinical features of sporadic amyotrophic lateral sclerosis (ALS) in Chinese patients, and to report data on the prognostic factors for survival.MethodsAll patients referred to...

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

Illness perceptions, mood and health-related quality of life in patients with amyotrophic lateral sclerosis

ABSTRACTBackgroundOver the past years, some studies in amyotrophic lateral sclerosis (ALS) have provided heterogeneous findings regarding demographic and clinical data as well as the impact of various prognostic factors. It is well known that these inconsistencies might be caused by a selection bias in hospital‐based data sets. In this study, we sought to further characterize this selection bias.MethodsWe compared hospital‐based data from the ALS center at Ulm University (UC; n = 3833; 1997–2021) with the population‐based ALS registry Swabia (SR; n = 852; 2010–2020).ResultsPatients from UC were younger (age of onset 60.9 [IQR 52.4–68.9] vs. 65.0 [57.0–72.7]), had a higher share of males (60.5% vs. 56.3%), a longer diagnostic delay (10.5 [IQR 6.4–18.4] months vs. 6.9 [IQR 3.4–12.1] months), a higher prevalence of the “definite” category according to El Escorial diagnostic criteria (60.9% vs. 11.2%), a higher share of familial cases (12.9% vs. 6.3%), a slower progression rate (points of ALS functional rating scale revised lost per month −0.54 [IQR −1.02 to −0.28] vs. −0.79 [IQR −1.47 to −0.43]), and (among all deceased patients) a higher share of percutaneous endoscopic gastrostomy (26.7% vs. 17.7%) and non‐invasive ventilation (34.3% vs. 25.3%).ConclusionsThe observed differences likely indicate a selection bias in hospital‐based data, which may be attributed, among others, to the willingness to travel large distances to a specialized center, the desire to participate in clinical studies, and the attitude toward life‐prolonging measures. These differences must be considered when interpreting and generalizing study results from hospital‐based populations.

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

Supported Treadmill Ambulation for Amyotrophic Lateral Sclerosis: A Pilot Study