Advances in brain science and computer technology in the past decade have led to exciting developments in brain-computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.

A comprehensive review of EEG-based brain-computer interface paradigms.

In this study, we propose and evaluate a technique known as common average referencing (CAR) to generate a more ideal reference electrode for microelectrode recordings. CAR is a computationally simple technique, and therefore amenable to both on-chip and real-time applications. CAR is commonly used in EEG, where it is necessary to identify small signal sources in very noisy recordings. To study the efficacy of common average referencing, we compared CAR to both referencing with a stainless steel bone-screw and a single microelectrode site. Data consisted of in vivo chronic recordings in anesthetized Sprague-Dawley rats drawn from prior studies, as well as previously unpublished data. By combining the data from multiple studies, we generated and analyzed one of the more comprehensive chronic neural recording datasets to date. Reference types were compared in terms of noise level, signal-to-noise ratio, and number of neurons recorded across days. Common average referencing was found to drastically outperform standard types of electrical referencing, reducing noise by >30%. As a result of the reduced noise floor, arrays referenced to a CAR yielded almost 60% more discernible neural units than traditional methods of electrical referencing. CAR should impart similar benefits to other microelectrode recording technologies-for example, chemical sensing-where similar differential recording concepts apply. In addition, we provide a mathematical justification for CAR using Gauss-Markov theorem and therefore help place the application of CAR into a theoretical context.

https://www.physiology.org/doi/pdf/10.1152/jn.90989.2008

Using a Common Average Reference to Improve Cortical Neuron Recordings From Microelectrode Arrays

Transcranial stimulation with weak direct current (DC) has been valuable in exploring the effect of cortical modulation on various neural networks. Less attention has been given, however, to cranial stimulation with low-intensity alternating current (AC). Reviewing and discussing these methods simultaneously with special attention to what is known about their mechanisms of action may provide new insights for the field of noninvasive brain stimulation. Direct current appears to modulate spontaneous neuronal activity in a polarity-dependent fashion with site-specific effects that are perpetuated throughout the brain via networks of interneuronal circuits, inducing significant effects on high-order cortical processes implicated in decision making, language, memory, sensory perception, and pain. AC stimulation has also been associated with a significant behavioral and clinical impact, but the mechanism of AC stimulation has been underinvestigated in comparison with DC stimulation. Even so, preliminary studies show that although AC stimulation has only modest effects on cortical excitability, it has been shown to induce synchronous changes in brain activity as measured by EEG activity. Thus, cranial AC stimulation may render its effects not by polarizing brain tissue, but rather via rhythmic stimulation that synchronizes and enhances the efficacy of endogenous neurophysiologic activity. Alternatively, secondary nonspecific central and peripheral effects may explain the clinical outcomes of DC or AC stimulation. Here the authors review what is known about DC and AC stimulation, and they discuss features that remain to be investigated.

http://charlesmccusker.com/pdf/HarvardMedicalSchoolStudy.pdf

Noninvasive Brain Stimulation with Low-Intensity Electrical Currents: Putative Mechanisms of Action for Direct and Alternating Current Stimulation

This paper describes a new and simple concept for fabricating low-cost, low-volume, easy-to-use microfluidic devices using threads. A thread can transport liquid via capillary wicking without the need of a barrier; as it is stainable, it is also a desirable material for displaying colorimetric results. When used in sewing, threads have 3D passageways in sewed materials. The wicking property and flexibility of thread make it particularly suitable to fabricate 3D microfluidic devices. Threads can also be used with other materials (e.g., paper) to make microfluidic devices for rapid qualitative or semiquantitative analysis. These thread-based and thread-paper-based devices have potential applications in human health diagnostics, environmental monitoring, and food safety analysis, and are particularly appropriate for the developing world or remote areas, because of their relatively low fabrication costs.

Thread as a Versatile Material for Low-Cost Microfluidic Diagnostics

Single-trial EEG classification of motor imagery using deep convolutional neural networks

Deep brain stimulation (DBS), also known as high frequency stimulation (HFS), is a well-established therapy for Parkinson's disease and essential tremor, and shows promise for the therapeutic control of epilepsy. However, the direct effect of DBS on neural elements close to the stimulating electrode remains an important unanswered question. Computational studies have suggested that HFS has a dual effect on neural elements inhibiting cell bodies, while exciting axons. Prior experiments have shown that sinusoidal HFS (50 Hz) can suppress synaptic and non-synaptic cellular activity in several in vitro epilepsy models, in all layers of the hippocampus. However, the effects of HFS on axons near the electrode are still unclear. In the present study, we tested the hypothesis that HFS suppresses axonal conduction in vitro. Sinusoidal HFS was applied to the alvear axon field of transverse rat hippocampal slices. The results show that HFS suppresses the alvear compound action potential (CAP) as well as the CA1 antidromic evoked potential (AEP). Complete suppression was observed as a 100% reduction in the amplitude of the evoked field potential for the duration of the stimulus. Evoked potential width and latency were not significantly affected by sinusoidal HFS. Suppression was dependent on HFS amplitude and frequency, but independent of stimulus duration and synaptic transmission. The frequency dependence of sinusoidal HFS is similar to that observed in clinical DBS, with maximal suppression between 50 and 200 Hz. HFS produced not only suppression of axonal conduction but also a correlated rise in extracellular potassium. These data provide new insights into the effects of HFS on neuronal elements, and show that HFS can block axonal activity through non-synaptic mechanisms.

https://iopscience.iop.org/article/10.1088/1741-2560/4/2/001/pdf

D M Durand

Papers

Suppression of axonal conduction by sinusoidal stimulation in rat hippocampus in vitro