Beyond the brain-computer interface: Decoding brain activity as a tool to understand neuronal mechanisms subtending cognition and behavior

Abstract

One of the major challenges in system neurosciences consists in developing techniques for estimating the cognitive information content in brain activity. This has an enormous potential in different domains spanning from clinical applications, cognitive enhancement to a better understanding of the neural bases of cognition. In this context, the inclusion of machine learning techniques to decode different aspects of human cognition and behavior and its use to develop brain–computer interfaces for applications in neuroprosthetics has supported a genuine revolution in the field. However, while these approaches have been shown quite successful for the study of the motor and sensory functions, success is still far from being reached when it comes to covert cognitive functions such as attention, motivation and decision making. While improvement in this field of BCIs is growing fast, a new research focus has emerged from the development of strategies for decoding neural activity. In this review, we aim at exploring how the advanced in decoding of brain activity is becoming a major neuroscience tool moving forward our understanding of brain functions, providing a robust theoretical framework to test predictions on the relationship between brain activity and cognition and behavior.

Distinct neural states encode task identity in frontal eye field and interact with its core spatial properties

“…We believe that these results provide novel insights on how the FEF organizes, from a computational perspective, attention and working memory information and recruits them in the context of specific tasks, and how potential pharmacological interventions can be targeted to improve these cognitive processes in patients affected by neurological diseases. Moreover, this knowledge leads to an improvement in the design of cognitive brain computer interface field (cBCI), opening the venue to the manipulation of the activity not only related to sensory or motor processes, but also related with a specific cognitive modality (Astrand et al, 2014;Loriette et al, 2022Loriette et al, , 2021.…”

Elevating Neuro-Linguistic Decoding: Deepening Neural-Device Interaction with RNN-GRU for Non-Invasive Language Decoding

“…One promising approach is the use of deep reinforcement learning networks (DNRNNs) and GRUs. The dynamic information embedded in neural signals linked to different cognitive functions can be decoded by these models, which are excellent at capturing temporal dependencies and sequential patterns [6]. Learning more about neural-device interaction is important not only for academics and researchers, but also for a wide range of applications in human-computer interaction, rehabilitation, and healthcare [7].…”

Decoding region-level visual functions from invasive EEG data

“…The potential to reconstruct visual information from the brain may rely on discerning specific neuronal spike patterns in familiar environments, facilitating the transformation of spatio-temporal features into meaningful images. While diverse architectures of artificial neural network (ANN)-based machines have been proposed, interpretability plays a crucial role in determining their adequacy as brain models [8, 9, 10, 11, 12, 13]. Hence, it is important not only to improve the accuracy of visual decoding but also to reveal the underlying mechanism in the mapping from visual stimuli to brain activity.…”