scholarly journals Classification of Articulator Movements and Movement Direction from Sensorimotor Cortex Activity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
E. Salari ◽  
Z. V. Freudenburg ◽  
M. P. Branco ◽  
E. J. Aarnoutse ◽  
M. J. Vansteensel ◽  
...  
Keyword(s):  
Neural Activity ◽  
Sensorimotor Cortex ◽  
Muscle Activity ◽  
Cortical Area ◽  
Brain Activity ◽  
Activity Patterns ◽  
Movement Direction ◽  
Computer Interfaces ◽  
Small Parts

Abstract For people suffering from severe paralysis, communication can be difficult or nearly impossible. Technology systems called brain-computer interfaces (BCIs) are being developed to assist these people with communication by using their brain activity to control a computer without any muscle activity. To benefit the development of BCIs that employ neural activity related to speech, we investigated if neural activity patterns related to different articulator movements can be distinguished from each other. We recorded with electrocorticography (ECoG), the neural activity related to different articulator movements in 4 epilepsy patients and classified which articulator participants moved based on the sensorimotor cortex activity patterns. The same was done for different movement directions of a single articulator, the tongue. In both experiments highly accurate classification was obtained, on average 92% for different articulators and 85% for different tongue directions. Furthermore, the data show that only a small part of the sensorimotor cortex is needed for classification (ca. 1 cm2). We show that recordings from small parts of the sensorimotor cortex contain information about different articulator movements which might be used for BCI control. Our results are of interest for BCI systems that aim to decode neural activity related to (actual or attempted) movements from a contained cortical area.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals Classification of Spatiotemporal Neural Activity Patterns in Brain Imaging Data

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Min Song ◽  
Minseok Kang ◽  
Hyeonsu Lee ◽  
Yong Jeong ◽  
Se-Bum Paik
Keyword(s):  
Brain Imaging ◽  
Neural Activity ◽  
Activity Patterns ◽  
Imaging Data ◽  
Brain Imaging Data

scholarly journals High-level cognition during story listening is reflected in high-order dynamic correlations in neural activity patterns

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lucy L. W. Owen ◽  
Thomas H. Chang ◽  
Jeremy R. Manning
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Dynamic Network ◽  
Brain Structures ◽  
High Order ◽  
Dynamic Correlations ◽  
Neuroimaging Data ◽  
Human Participants ◽  
High Level

AbstractOur thoughts arise from coordinated patterns of interactions between brain structures that change with our ongoing experiences. High-order dynamic correlations in neural activity patterns reflect different subgraphs of the brain’s functional connectome that display homologous lower-level dynamic correlations. Here we test the hypothesis that high-level cognition is reflected in high-order dynamic correlations in brain activity patterns. We develop an approach to estimating high-order dynamic correlations in timeseries data, and we apply the approach to neuroimaging data collected as human participants either listen to a ten-minute story or listen to a temporally scrambled version of the story. We train across-participant pattern classifiers to decode (in held-out data) when in the session each neural activity snapshot was collected. We find that classifiers trained to decode from high-order dynamic correlations yield the best performance on data collected as participants listened to the (unscrambled) story. By contrast, classifiers trained to decode data from scrambled versions of the story yielded the best performance when they were trained using first-order dynamic correlations or non-correlational activity patterns. We suggest that as our thoughts become more complex, they are reflected in higher-order patterns of dynamic network interactions throughout the brain.

scholarly journals Translation of Brain Activity Patterns of a user into Commands using Electroencephalography (EEG)

2021 ◽  
pp. 62-68
Author(s):  
Ranjana B. Jadekar ◽  
A. R. Sindhu ◽  
M. T. Vinay
Keyword(s):  
Environmental Control ◽  
Brain Activity ◽  
Activity Patterns ◽  
Eeg Signals ◽  
Computer Interfaces ◽  
Control Option ◽  
Electrophysiological Signals ◽  
Interactive Application ◽  
The Brain ◽  
Bci System

Brain-Computer Interfaces (BCI) are systems that can translate the brain activity patterns of a user into messages or commands for an interactive application. The brain activity which is processed by the BCI systems is usually measured using Electroencephalography (EEG). The BCI system uses oscillatory Electroencephalography (EEG) signals, recorded using specific mental activity, as input and provides a control option by its output. A brain-computer interface uses electrophysiological signals to control the remote devices. They consist of electrodes applied to the scalp of an individual or worn in an electrode cap. The computer processes the EEG signals and uses it in order to accomplish tasks such as communication and environmental control.

scholarly journals Classification of EEG signal using EACA based approach at SSVEP-BCI

2021 ◽  
Vol 10 (3) ◽  
pp. 717
Author(s):  
Ashwini S. R. ◽  
H. C. Nagaraj
Keyword(s):  
Classification Accuracy ◽  
Communication Channel ◽  
Brain Activity ◽  
Computer Interfaces ◽  
Computer Device ◽  
Optimal Classification ◽  
Data Signal ◽  
The Brain ◽  
Identification Probability

The brain-computer-interfaces (BCI) can also be referred towards a mindmachine interface that can provide a non-muscular communication channel in between the computer device and human brain. To measure the brain activity, electroencephalography (EEG) has been widely utilized in the applications of BCI to work system in real-time. It has been analyzed that the identification probability performed with other methodologies do not provide optimal classification accuracy. Therefore, it is required to focus on the process of feature extraction to achieve maximum classification accuracy. In this paper, a novel process of data-driven spatial has been proposed to improve the detection of steady state visually evoked potentials (SSVEPs) at BCI. Here, EACA has been proposed, which can develop the reproducibility of SSVEP across many trails. Further this can be utilized to improve the SSVEP from a noisy data signal by eliminating the activities of EEG background. In the simulation process, the SSVEP dataset recorded from given 11 subjects are considered. To validate the performance, the state-of-art method is considered to compare with the EDCA based proposed approach.

scholarly journals Tidal volume and respiration phase modulate cortico-muscular communication

2020 ◽  
Author(s):  
Daniel S. Kluger ◽  
Joachim Gross
Keyword(s):  
Tidal Volume ◽  
Neural Activity ◽  
Muscle Activity ◽  
Motor System ◽  
Brain Activity ◽  
Potential Influence ◽  
Brain Areas ◽  
Normal Breathing ◽  
Human Motor ◽  
Respiratory Rhythms

AbstractRecent studies in animals have convincingly demonstrated that respiration cyclically modulates oscillatory neural activity across diverse brain areas. To what extent this generalises to humans in a way that is relevant for behaviour is yet unclear. We used magnetoencephalography (MEG) to assess the potential influence of tidal volume and respiration phase on the human motor system. We obtained simultaneous recordings of brain activity, muscle activity, and respiration while participants performed an isometric contraction task. We used corticomuscular coherence as a measure of efficient long-range cortico-peripheral communication.We found coherence within the beta range over sensorimotor cortex to be reduced during voluntary deep compared to involuntary normal breathing. Moreover, beta coherence was found to be cyclically modulated by respiration phase in both conditions. Overall, these results demonstrate how respiratory rhythms actively influence brain oscillations in an effort to synchronise neural activity for the sake of computational efficiency. Intriguing questions remain with regard to the shape of these modulatory processes and how they influence perception, cognition, and behaviour.

scholarly journals Brain-inspired spiking neural networks for decoding and understanding muscle activity and kinematics from electroencephalography signals during hand movements

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kaushalya Kumarasinghe ◽  
Nikola Kasabov ◽  
Denise Taylor
Keyword(s):  
Artificial Intelligence ◽  
Upper Limb ◽  
Muscle Activity ◽  
Brain Activity ◽  
Strongly Correlated ◽  
Population Activity ◽  
Non Invasive ◽  
Computer Interfaces ◽  
Spike Sequences ◽  
Functional Movements

AbstractCompared to the abilities of the animal brain, many Artificial Intelligence systems have limitations which emphasise the need for a Brain-Inspired Artificial Intelligence paradigm. This paper proposes a novel Brain-Inspired Spiking Neural Network (BI-SNN) model for incremental learning of spike sequences. BI-SNN maps spiking activity from input channels into a high dimensional source-space which enhances the evolution of polychronising spiking neural populations. We applied the BI-SNN to predict muscle activity and kinematics from electroencephalography signals during upper limb functional movements. The BI-SNN extends our previously proposed eSPANNet computational model by integrating it with the ‘NeuCube’ brain-inspired SNN architecture. We show that BI-SNN can successfully predict continuous muscle activity and kinematics of upper-limb. The experimental results confirmed that the BI-SNN resulted in strongly correlated population activity and demonstrated the feasibility for real-time prediction. In contrast to the majority of Brain–Computer Interfaces (BCIs) that constitute a ‘black box’, BI-SNN provide quantitative and visual feedback about the related brain activity. This study is one of the first attempts to examine the feasibility of finding neural correlates of muscle activity and kinematics from electroencephalography using a brain-inspired computational paradigm. The findings suggest that BI-SNN is a better neural decoder for non-invasive BCI.

Sign in / Sign up

Export Citation Format

Share Document