EEG Based Brain-Machine Interfacing: Navigation of Mobile Robotic Device

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.

People with severe physical disabilities are not able of using standard robotic wheelchairs, which generally demand some motor skills, and therefore total usage of associate muscles. Robotic wheelchairs commanded by Brain-Computer Interfaces (BCIs) based on Electroencephalography (EEG) have demonstrated to be an alternative for these end-users. In general, existing robotic wheelchairs commanded by BCIs require special platforms adapted to the EEG-BCI, and end-users need to attend a long training process for safely drive these devices. But many times these potential users do not have access to training sessions; due to mobility problems or technology access restrictions. This study proposes an EEG-based BCI with customizable configuration to be used in cloud architectures for remote control of robotic wheelchairs. This research explores two types of Steady State Visual Evoked Potential (SSVEP)-based BCI by applying Canonical Correlation Analysis (CCA) and compressive sensing (CS) as a novelty, adopting in one free calibration, and other including a calibration stage. The free-calibrated SSVEP recognition approach (CS-ncCCA) using compression ratio (CR) at 60% obtained accuracy (ACC) of 85% and information transfer rate (ITR) of 102 bits per minute (bpm), whereas the calibrated BCI (CS-wcCCA) applying also CR at 62% achieved ACC of 85% and ITR of 195 bpm. As a hilghlight, the proposed BCI allows significant reduction of the transmitted file size, and improves the communication latency that may be useful in remote and Cloud robotics applications, such as for users with severe motor disabilities, to train driving safely robotic wheelchairs via IoT.

A Brain-Computer interface (BCI) is a communication system that enables the generation of a control signal from brain signals such as sensorymotor rhythms and evoked potentials; therefore, it constitutes a novel communication option for people with severe motor disabilities (such as Amyotrophic Lateral Sclerosis patients). This paper presents the development of a P300-based BCI. This prototype uses a homemade six-channel electroencephalograph for the acquisition of the signals, and a visual stimulation matrix; since this matrix contains letters of the alphabet as well as images associated to them, it permits word-writing and the elaboration of messages with the images. To process the signals the software BCI2000 and MATLAB 7.0 were used. The latter was used to program three linear translation algorithms (Stepwise Linear Discriminant Analysis, Lineal Discriminant Analysis and Least Squares) to convert the brain signals into communication signals. These algorithms had a classification accuracy of 90.73 %, 95.75 % and 89.45 % respectively, when using raw data; and of 90.78%, 49.48 % and 53.9 %, when data was previously common-average filtered. The experimental setup was tested in ten healthy volunteers; 5 of them got a 100% success, 1 a 90% success, 2 an around 70% success and 2 a 50% success, in the online free-spelling tests.

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.

ABSTRACT
\n
\nPutri, Anita Kurnia. 2020. “Analisis Kontrastif Adverbia Tinkatan Penanda Negasi dalam Bahasa Jepang dan Bahasa Jepang”. Thesis, department of Japanese Language and Culture, Faculty of Humanities, Diponegoro University. The Advisor: Lina Rosliana, S.S., M.Hum.
\n
\nThe problems that will be discussed in this thesis include: 1. What is the structure and meaning of the adverb in the level of negation markers in Japanese sentences? 2. What is the structure and meaning of the adverb at the level of the negation marker in Indonesian sentences? 3. How are the adverbs of the level of negation markers similar and different in Japanese and Indonesian sentences?
\n
\nMeanwhile, the purpose of this study is to describe the structure and meaning of adverbs in Japanese and Indonesian sentences. After knowing the structure and meaning, we will examine the similarities and differences in the structure and meaning of the adverbial levels of the negation markers of the two languages.
\n
\nIn analyzing the data, this study used the agih method followed by contrastive techniques. The data were obtained from the Japanese language website and news articles in Indonesian. Then the data is presented in words or descriptive.
\n
\nBased on the research, the structural similarities are both followed by verbs, adjectives. While the difference, adverbs in Japanese can be followed by phrases or clauses and negative forms, Indonesian adverbial followed by nouns. In meaning, the adverbs of the level of negation markers in Japanese and Indonesian indicates the meaning of the situation, the action is under adequate. And in terms of meaning it has 7 differences.
\n
\nKeywords : contrastive, adverbs, teido no fukushi
\n

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

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.

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.

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

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) can provide communication and control to people who are totally paralyzed. BCIs can use noninvasive or invasive methods for recording the brain signals that convey the user's commands. Whereas noninvasive BCIs are already in use for simple applications, it has been widely assumed that only invasive BCIs, which use electrodes implanted in the brain, can provide multidimensional movement control of a robotic arm or a neuroprosthesis. We now show that a noninvasive BCI that uses scalp-recorded electroencephalographic activity and an adaptive algorithm can provide humans, including people with spinal cord injuries, with multidimensional point-to-point movement control that falls within the range of that reported with invasive methods in monkeys. In movement time, precision, and accuracy, the results are comparable to those with invasive BCIs. The adaptive algorithm used in this noninvasive BCI identifies and focuses on the electroencephalographic features that the person is best able to control and encourages further improvement in that control. The results suggest that people with severe motor disabilities could use brain signals to operate a robotic arm or a neuroprosthesis without needing to have electrodes implanted in their brains.

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.

Brain-computer interfaces (BCIs) can provide non-muscular communication and control for people with severe motor disabilities. Current BCIs use a variety of invasive and non-invasive methods to record brain signals and a variety of signal processing methods. Whatever the recording and processing methods used, BCI performance (e.g. the ability of a BCI to control movement of a computer cursor) is highly variable and, by the standards applied to neuromuscular control, could be described as ataxic. In an effort to understand this imperfection, this paper discusses the relevance of two principles that underlie the brain's normal motor outputs. The first principle is that motor outputs are normally produced by the combined activity of many CNS areas, from the cortex to the spinal cord. Together, these areas produce appropriate control of the spinal motoneurons that activate muscles. The second principle is that the acquisition and life-long preservation of motor skills depends on continual adaptive plasticity throughout the CNS. This plasticity optimizes the control of spinal motoneurons. In the light of these two principles, a BCI may be viewed as a system that changes the outcome of CNS activity from control of spinal motoneurons to, instead, control of the cortical (or other) area whose signals are used by the BCI to determine the user's intent. In essence, a BCI attempts to assign to cortical neurons the role normally performed by spinal motoneurons. Thus, a BCI requires that the many CNS areas involved in producing normal motor actions change their roles so as to optimize the control of cortical neurons rather than spinal motoneurons. The disconcerting variability of BCI performance may stem in large part from the challenge presented by the need for this unnatural adaptation. This difficulty might be reduced, and BCI development might thereby benefit, by adopting a 'goal-selection' rather than a 'process- control' strategy. In 'process control', a BCI manages all the intricate high-speed interactions involved in movement. In 'goal selection', by contrast, the BCI simply communicates the user's goal to software that handles the high-speed interactions needed to achieve the goal. Not only is 'goal selection' less demanding, but also, by delegating lower-level aspects of motor control to another structure (rather than requiring that the cortex do everything), it more closely resembles the distributed operation characteristic of normal motor control.

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.