Role of Multifocal Visually Evoked Potential as a Biomarker of Demyelination, Spontaneous Remyelination, and Myelin Repair in Multiple Sclerosis

Multiple sclerosis (MS) is a complex disease of the central nervous system (CNS), characterized by inflammation, demyelination, neuro-axonal loss, and gliosis. Inflammatory demyelinating lesions are a hallmark of the disease. Spontaneous remyelination, however, is often incomplete and strategies that promote remyelination are needed. As a result, accurate and sensitive in vivo measures of remyelination are necessary. The visual pathway provides a unique opportunity for in vivo assessment of myelin damage and repair in the MS-affected brain since it is highly susceptible to damage in MS and is a very frequent site of MS lesions. The visually evoked potential (VEP), an event-related potential generated by the striate cortex in response to visual stimulation, is uniquely placed to serve as a biomarker of the myelination along the visual pathway. The multifocal VEP (mfVEP) represents a most recent addition to the array of VEP stimulations. This article provides a current view on the role of mfVEP as a biomarker of demyelination, spontaneous remyelination, and myelin repair in MS.

A Decadal Walk on BCI Technology

In this chapter, the authors take a walkthrough in BCI technology. At first, they took a closer look into the kind of waves that are being generated by our brain (i.e., the EEG and ECoG waves). In the next section, they have discussed about patients affected by CLIS and ALS-CLIS and how they can be treated or be benefitted using BCI technology. Visually evoked potential-based BCI technology has also been thoroughly discussed in this chapter. The application of machine learning and deep learning in this field are also being discussed with the need for feature engineering in this paradigm also been said. In the final section, they have done a thorough literature survey on various research-related to this field with proposed methodology and results.

A Bipolar-Channel Hybrid Brain-Computer Interface System for Home Automation Control Utilizing Steady-State Visually Evoked Potential and Eye-Blink Signals

The goal of this study was to develop and validate a hybrid brain-computer interface (BCI) system for home automation control. Over the past decade, BCIs represent a promising possibility in the field of medical (e.g., neuronal rehabilitation), educational, mind reading, and remote communication. However, BCI is still difficult to use in daily life because of the challenges of the unfriendly head device, lower classification accuracy, high cost, and complex operation. In this study, we propose a hybrid BCI system for home automation control with two brain signals acquiring electrodes and simple tasks, which only requires the subject to focus on the stimulus and eye blink. The stimulus is utilized to select commands by generating steady-state visually evoked potential (SSVEP). The single eye blinks (i.e., confirm the selection) and double eye blinks (i.e., deny and re-selection) are employed to calibrate the SSVEP command. Besides that, the short-time Fourier transform and convolution neural network algorithms are utilized for feature extraction and classification, respectively. The results show that the proposed system could provide 38 control commands with a 2 s time window and a good accuracy (i.e., 96.92%) using one bipolar electroencephalogram (EEG) channel. This work presents a novel BCI approach for the home automation application based on SSVEP and eye blink signals, which could be useful for the disabled. In addition, the provided strategy of this study—a friendly channel configuration (i.e., one bipolar EEG channel), high accuracy, multiple commands, and short response time—might also offer a reference for the other BCI controlled applications.