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  • Updated 06.22.2022
  • Released 12.10.2014
  • Expires For CME 06.22.2025

Neurotechnology: brain-computer and brain-machine interfaces

Introduction

Overview

Device interfaces with the brain is one of the most promising areas of research in the diagnosis and treatment of disorders of the nervous system. The ability to monitor brain electrical and chemical activity in real time and with noninvasive or minimally invasive techniques is crucial for both the understanding of nervous system functioning in health and disease and the development of effective treatment options for those disorders. Moreover, the ability to restore the diseased nervous system to an intact and normal-functioning state or substitute lost function with brain-actuated assistive devices is crucially dependent on techniques to translate that monitoring information into effective treatment modalities, ie, to stimulate brain tissue and modulate brain activity.

Although the terms brain-computer interface (BCI) and brain-machine interface (BMI) have frequently been used interchangeably in the past, it is increasingly obvious that two separate applications have evolved over the past 2 decades. Brain-computer interface refers to increasingly sophisticated computational analyses and processing of brain function (as evidenced by noninvasive or minimally-invasive techniques such as electroencephalography [EEG]) to enable the individual to control a neuroprosthetic device (eg, a computer or a robotic arm) or improve function impaired by a stroke or central nervous system trauma (ie, neurorehabilitation). Brain-machine interface refers to the actual interface between nervous system tissue and a device (usually referred to as an electrode); thus, BMI is invasive to varying degrees. BMI is at present predominantly a research lab endeavor, an evolving field where techniques and materials at the micro to nano level are utilized to better monitor and modulate brain function with increasing precision. However, BCI studies usually utilize standard techniques for monitoring brain electrical activity, EEG or electrocorticography (ECoG), in patients who undergo temporary electrode placement surgically, eg, for assessing suitability for epilepsy surgery. BMI research is centered on improving the electrodes themselves for recording or stimulating brain electrical and/or chemical activity. Because the BCI community is much larger and clinically relevant (see below), it is not surprising for many to assume that BCI encompasses the entire “brain-device interface” field. In reality, BCI emphasizes the “computer” aspect; BMI emphasizes the “interface” aspect.

The international Brain-Computer Interface Society broadly defines BCI as the ensemble of “technologies that enable people to interact with the world through brain signals.” As such, BCI must be distinguished from sensory prosthetics (eg, retinal or cochlear implants), from brain imaging methods used for diagnosis (eg, EEG for the diagnosis of sleep disorders or epilepsy), and from brain stimulation neuromodulation approaches.

One example of neuromodulation, deep brain stimulation (DBS), has proven to be the greatest advance in the treatment of Parkinson disease since the demonstration of the effectiveness of L-dopa nearly 50 years ago. DBS has become such an important treatment modality for disorders ranging from Parkinson disease and other movement disorders to refractory epilepsy and mood disorders such as severe depression that DBS warrants a separate Medlink article itself (45).

A major distinction concerns the type of tissue targeted by the neural interface hardware. According to the prevalent definition (91), a BCI or BMI in the strict sense should only rely on the activity of the central nervous system (CNS)—brain or spinal cord. Therefore, techniques involving the peripheral nervous system and muscles will be excluded from this overview.

Given the robust clinical applications of BCI that have evolved over the past several decades, it is not surprising that a simple PubMed search for “brain-computer interface” in the title or abstract done in April 2022 yielded nearly five times as many publications as a search for “brain-machine interface.” Although BMI is frequently used to refer to BCI as described above, the reverse is rarely the case: BCI is infrequently used to describe a publication on materials or techniques to enhance a device in direct contact with brain tissue (ie, BMI). The expansion of publications involving BCI into rehabilitation (including treatment of disorders such as alcoholism) in addition to BCI for control of neuroprostheses has resulted in efforts to simplify the techniques utilized for data collection. Ease of application and tolerance by patients of EEG electrodes (eg, scalp caps) have become important aspects as BCI techniques spread to the large clinical audience of rehabilitation for poststroke and neurotrauma (brain and spinal cord injury) patients. Major progress has been made in the analysis and processing of the brain electrical activity recorded by the EEG, largely thanks to advances in machine learning and artificial intelligence (AI).

Only brief attention is given in this article to brain stimulation techniques such as transcranial magnetic stimulation and techniques that stimulate the spinal cord (eg, for chronic pain or bladder dysfunction), whereas those targeting the peripheral nervous system (eg, for pain), or muscles (eg, for restoration of function) are entirely excluded. This article will also not consider novel techniques with great potential for treating disorders from neurodegenerative diseases to brain tumors such as MRI-guided focused ultrasound (MRgFUS). This article focuses primarily on what might be considered “pure” BCI and BMI techniques. Additionally, techniques that may be of great value in animal models but that are unlikely to be used in humans in the near future, such as optogenetics, are not reviewed here.

Review articles on the field of BCI and BMI are appearing with increasing rapidity as its potential for both understanding and restoring brain function is realized (91; Wolpaw et al 2011; 11; 88; 18; 92). It is estimated there are more than 100,000 quadriplegic patients in the United States alone, and the incidence of stroke in the United States is approximately 200 per 100,000 population (ie, nearly 700,000 per year). Therefore, the need for both better understanding and more effective treatments for these patients (not to mention the larger number of patients with nervous system disorders ranging from depression to epilepsy to Parkinson disease) has compelling motivations—both humanitarian and economic. The potential for commercialization of BCI in particular has been acknowledged in the journal Nature (25).

The “technical aspects” portion of this article is divided into two sections: the first considering BCI and the second BMI.

Key points

• The brain-machine interface (BMI) is the communication link between biology and technology, ie, the translation of brain electrical and chemical activity into information that can then be “computed” to feed information to a prosthesis to correct a brain disorder or replace lost function.


• The brain-machine interface involves computationally demanding algorithms to process the vast amounts of brain electrical or chemical activity data acquired.


• The BMI involves precise (potentially cellular-level) recording of brain electrical or chemical activity to better understand brain function. The BMI can also involve precise stimulation, eg, cochlear retinal prostheses.


• The brain-computer interface (BCI) can be divided into invasive or noninvasive techniques depending on whether a surgical procedure is involved to implant the device. At present, the BMI involves a surgical procedure and, thus, is invasive.


• Noninvasive BCI techniques are not necessarily preferable to invasive techniques, as usually they are less precise.

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