Components Of Brain-computer Interface Research Articles

Intracortical recording interfaces: current challenges to chronic recording function.

Brain Computer Interfaces (BCIs) offer significant hope to tetraplegic and paraplegic individuals. This technology relies on extracting and translating motor intent to facilitate control of a computer cursor or to enable fine control of an external assistive device such as a prosthetic limb. Intracortical recording interfaces (IRIs) are critical components of BCIs and consist of arrays of penetrating electrodes that are implanted into the motor cortex of the brain. These multielectrode arrays (MEAs) are responsible for recording and conducting neural signals from local ensembles of neurons in the motor cortex with the high speed and spatiotemporal resolution that is required for exercising control of external assistive prostheses. Recent design and technological innovations in the field have led to significant improvements in BCI function. However, long-term (chronic) BCI function is severely compromised by short-term (acute) IRI recording failure. In this review, we will discuss the design and function of current IRIs. We will also review a host of recent advances that contribute significantly to our overall understanding of the cellular and molecular events that lead to acute recording failure of these invasive implants. We will also present recent improvements to IRI design and provide insights into the futuristic design of more chronically functional IRIs.

Brain–computer interfacing: more than the sum of its parts

The performance of non-invasive electroencephalogram-based (EEG) brain---computer interfaces (BCIs) has improved significantly in recent years. However, remaining challenges include the non-stationarity and the low signal-to-noise ratio of the EEG, which limit the bandwidth and hence the available applications. Optimization of both individual components of BCIs and the interrelationship between them is crucial to enhance bandwidth. In other words, neuroscientific knowledge and machine learning need to be optimized by considering concepts from human---computer interaction research and usability. In this paper, we present results of ongoing relevant research in our lab that addresses several important issues for BCIs based on the detection of transient changes in oscillatory EEG activity. First, we report on the long-term stability and robustness of detection of oscillatory EEG components modulated by distinct mental tasks, and show that the use of mental task pairs "mental subtraction versus motor imagery" achieves robust and reliable performance (Cohen's ? > 0.6) in seven out of nine subjects over a period of 4 days. Second, we report on restricted Boltzmann machines (RBMs) as promising tools for the recognition of oscillatory EEG patterns. In an off-line BCI simulation we computed average peak accuracies, averaged over ten subjects, of 80.8 ± 7.2 %. Third, we present the basic framework of the context-aware hybrid Graz-BCI that allows interacting with the massive multiplayer online role playing game World of Warcraft. We show how a more integrated design approach that considers all components of BCIs, their interrelationships, other input signals and contextual information can increase interaction efficacy.

Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects.

Brain-computer interfaces (BCIs) allow their users to communicate or control external devices using brain signals rather than the brain's normal output pathways of peripheral nerves and muscles. Motivated by the hope of restoring independence to severely disabled individuals and by interest in further extending human control of external systems, researchers from many fields are engaged in this challenging new work. BCI research and development have grown explosively over the past two decades. Efforts have recently begun to provide laboratory-validated BCI systems to severely disabled individuals for real-world applications. In this review, we discuss the current status and future prospects of BCI technology and its clinical applications. We will define BCI, review the BCI-relevant signals from the human brain, and describe the functional components of BCIs. We will also review current clinical applications of BCI technology, and identify potential users and potential applications. Finally, we will discuss current limitations of BCI technology, impediments to its widespread clinical use, and expectations for the future.