Network Brain-Computer Interface (nBCI): An Alternative Approach for Cognitive Prosthetics

Abstract

Brain computer interfaces (BCIs) have been applied to sensorimotor systems for many years. However, BCI technology has broad potential beyond sensorimotor systems. The emerging field of cognitive prosthetics, for example, promises to improve learning and memory for patients with cognitive impairment. Unfortunately, our understanding of the neural mechanisms underlying these cognitive processes remains limited in part due to the extensive individual variability in neural coding and circuit function. As a consequence, the development of methods to ascertain optimal control signals for cognitive decoding and restoration remains an active area of inquiry. To advance the field, robust tools are required to quantify time-varying and task-dependent brain states predictive of cognitive performance. Here, we suggest that network science is a natural language in which to formulate and apply such tools. In support of our argument, we offer a simple demonstration of the feasibility of a network approach to BCI control signals, which we refer to as network BCI (nBCI). Finally, in a single subject example, we show that nBCI can reliably predict online cognitive performance and is superior to certain common spectral approaches currently used in BCIs. Our review of the literature and preliminary findings support the notion that nBCI could provide a powerful approach for future applications in cognitive prosthetics.

Brain Computer Interfaces for Improving the Quality of Life of Older Adults and Elderly Patients

“…BCI has also been found to be helpful in identifying deficits and improving social skills in patients with autism through the use of BCI-assisted social games ( Amaral et al, 2018 ). Other research has focused on systems to test memory capacity and cognitive level ( Burke et al, 2015 ; Buch et al, 2018 ).…”

“…Brain computer interface technology could be one potential tool for restoring learning and improving memory, attention, and consciousness for cognitively impaired elderly patients ( Buch et al, 2018 ). For instance, non-invasive BCIs have been be used for restoring memory and planning using electromagnetic stimulation and biofeedback that modulate activity in a patient’s brain as part of a rehabilitation program.…”

EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network

“…Recent research indicates that combining bridge neurofeedback, connectomics, and network control theory can better understand the current frontier of human cognition [7]. Functional connectivity often is referred to as the addition of quantitative measures of coordinated activity [8]. The functional connectivity model can provide a cost-effective approach to differentiate and integrate information.…”

Electrocorticography and stereo EEG provide distinct measures of brain connectivity: Implications for network models

“…These networks are analyzed using graph theory 10,14,19 or neural mass models, which simulate seizure-like activity and probe the effects of different surgical interventions 11,13 . Intracranial EEG network models are also used to study networks activated during normal brain function, for example in recent studies probing cognition 20 and attention 21 . The majority of these studies use patients implanted with ECoG, often supplemented with depth electrodes placed in the hippocampus.…”