A small, portable, battery-powered brain-computer interface system for motor rehabilitation

Noninvasive Electroencephalography Equipment for Assistive, Adaptive, and Rehabilitative Brain-Computer Interfaces: A Systematic Literature Review

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.

A Robust Low-Cost EEG Motor Imagery-Based Brain-Computer Interface

Motor imagery (MI) based Brain-Computer Interfaces (BCIs) are a viable option for giving locked-in syndrome patients independence and communicability. BCIs comprising expensive medical-grade EEG systems evaluated in carefully-controlled, artificial environments are impractical for take-home use. Previous studies evaluated low-cost systems; however, performance was suboptimal or inconclusive. Here we evaluated a low-cost EEG system, OpenBCI, in a natural environment and leveraged neurofeedback, deep learning, and wider temporal windows to improve performance. $\mu-$rhythm data collected over the sensorimotor cortex from healthy participants performing relaxation and right-handed MI tasks were used to train a multi-layer perceptron binary classifier using deep learning. We showed that our method outperforms previous OpenBCI MI-based BCIs, thereby extending the BCI capabilities of this low-cost device.

Interfacing with the nervous system: a review of current bioelectric technologies

The aim of this study is to discuss the state of the art with regard to established or promising bioelectric therapies meant to alter or control neurologic function. We present recent reports on bioelectric technologies that interface with the nervous system at three potential sites-(1) the end organ, (2) the peripheral nervous system, and (3) the central nervous system-while exploring practical and clinical considerations. A literature search was executed on PubMed, IEEE, and Web of Science databases. A review of the current literature was conducted to examine functional and histomorphological effects of neuroprosthetic interfaces with a focus on end-organ, peripheral, and central nervous system interfaces. Innovations in bioelectric technologies are providing increasing selectivity in stimulating distinct nerve fiber populations in order to activate discrete muscles. Significant advances in electrode array design focus on increasing selectivity, stability, and functionality of implantable neuroprosthetics. The application of neuroprosthetics to paretic nerves or even directly stimulating or recording from the central nervous system holds great potential in advancing the field of nerve and tissue bioelectric engineering and contributing to clinical care. Although current physiotherapeutic and surgical treatments seek to restore function, structure, or comfort, they bear significant limitations in enabling cosmetic or functional recovery. Instead, the introduction of bioelectric technology may play a role in the restoration of function in patients with neurologic deficits.

Performance Assessment of a Custom, Portable, and Low-Cost Brain-Computer Interface Platform

OBJECTIVE

Conventional brain-computer interfaces (BCIs) are often expensive, complex to operate, and lack portability, which confines their use to laboratory settings. Portable, inexpensive BCIs can mitigate these problems, but it remains unclear whether their low-cost design compromises their performance. Therefore, we developed a portable, low-cost BCI and compared its performance to that of a conventional BCI.

METHODS

The BCI was assembled by integrating a custom electroencephalogram (EEG) amplifier with an open-source microcontroller and a touchscreen. The function of the amplifier was first validated against a commercial bioamplifier, followed by a head-to-head comparison between the custom BCI (using four EEG channels) and a conventional 32-channel BCI. Specifically, five able-bodied subjects were cued to alternate between hand opening/closing and remaining motionless while the BCI decoded their movement state in real time and provided visual feedback through a light emitting diode. Subjects repeated the above task for a total of 10 trials, and were unaware of which system was being used. The performance in each trial was defined as the temporal correlation between the cues and the decoded states.

RESULTS

The EEG data simultaneously acquired with the custom and commercial amplifiers were visually similar and highly correlated ( ρ = 0.79). The decoding performances of the custom and conventional BCIs averaged across trials and subjects were 0.70 ± 0.12 and 0.68 ± 0.10, respectively, and were not significantly different.

CONCLUSION

The performance of our portable, low-cost BCI is comparable to that of the conventional BCIs.

SIGNIFICANCE

Platforms, such as the one developed here, are suitable for BCI applications outside of a laboratory.

Feasibility of an ultra-low power digital signal processor platform as a basis for a fully implantable brain-computer interface system

A fully implantable brain-computer interface (BCI) can be a practical tool to restore independence to those affected by spinal cord injury. We envision that such a BCI system will invasively acquire brain signals (e.g. electrocorticogram) and translate them into control commands for external prostheses. The feasibility of such a system was tested by implementing its benchtop analogue, centered around a commercial, ultra-low power (ULP) digital signal processor (DSP, TMS320C5517, Texas Instruments). A suite of signal processing and BCI algorithms, including (de)multiplexing, Fast Fourier Transform, power spectral density, principal component analysis, linear discriminant analysis, Bayes rule, and finite state machine was implemented and tested in the DSP. The system's signal acquisition fidelity was tested and characterized by acquiring harmonic signals from a function generator. In addition, the BCI decoding performance was tested, first with signals from a function generator, and subsequently using human electroencephalogram (EEG) during eyes opening and closing task. On average, the system spent 322 ms to process and analyze 2 s of data. Crosstalk (<;-65 dB) and harmonic distortion (~1%) were minimal. Timing jitter averaged 49 μs per 1000 ms. The online BCI decoding accuracies were 100% for both function generator and EEG data. These results show that a complex BCI algorithm can be executed on an ULP DSP without compromising performance. This suggests that the proposed hardware platform may be used as a basis for future, fully implantable BCI systems.