2,606 research outputs found
Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG
This study investigates the indications of non-linear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven non-linear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov exponent, non-linear prediction error and approximate entropy has been used for analysing the EEG data obtained during self paced voluntary finger-movement. The results have demonstrated that there are clear indications of non-linearity in the EEG signals. However the rejection of the null hypothesis of non-linearity rate varied based on different parameter settings demonstrating significance of embedding dimension and time lag parameters for capturing underlying non-linear dynamics in the signals. Across non-linear test statistics, the highest degree of non-linearity was indicated by approximate entropy (APEN) feature regardless of the parameter settings
Decoding Imagined 3D Hand Movement Trajectories From EEG: Evidence to Support the Use of Mu, Beta, and Low Gamma Oscillations
Objective: To date, motion trajectory prediction (MTP) of a limb from non-invasive electroencephalography (EEG) has relied, primarily, on band-pass filtered samples of EEG potentials i.e., the potential time-series model. Most MTP studies involve decoding 2D and 3D arm movements i.e., executed arm movements. Decoding of observed or imagined 3D movements has been demonstrated with limited success and only reported in a few studies. MTP studies normally use EEG potentials filtered in the low delta (~1 Hz) band for reconstructing the trajectory of an executed or an imagined/observed movement. In contrast to MTP, multiclass classification based sensorimotor rhythm brain-computer interfaces aim to classify movements using the power spectral density of mu (8–12 Hz) and beta (12–28 Hz) bands.Approach: We investigated if replacing the standard potentials time-series input with a power spectral density based bandpower time-series improves trajectory decoding accuracy of kinesthetically imagined 3D hand movement tasks (i.e., imagined 3D trajectory of the hand joint) and whether imagined 3D hand movements kinematics are encoded also in mu and beta bands. Twelve naïve subjects were asked to generate or imagine generating pointing movements with their right dominant arm to four targets distributed in 3D space in synchrony with an auditory cue (beep).Main results: Using the bandpower time-series based model, the highest decoding accuracy for motor execution was observed in mu and beta bands whilst for imagined movements the low gamma (28–40 Hz) band was also observed to improve decoding accuracy for some subjects. Moreover, for both (executed and imagined) movements, the bandpower time-series model with mu, beta, and low gamma bands produced significantly higher reconstruction accuracy than the commonly used potential time-series model and delta oscillations.Significance: Contrary to many studies that investigated only executed hand movements and recommend using delta oscillations for decoding directional information of a single limb joint, our findings suggest that motor kinematics for imagined movements are reflected mostly in power spectral density of mu, beta and low gamma bands, and that these bands may be most informative for decoding 3D trajectories of imagined limb movements
Advancing Brain-Computer Interface System Performance in Hand Trajectory Estimation with NeuroKinect
Brain-computer interface (BCI) technology enables direct communication
between the brain and external devices, allowing individuals to control their
environment using brain signals. However, existing BCI approaches face three
critical challenges that hinder their practicality and effectiveness: a)
time-consuming preprocessing algorithms, b) inappropriate loss function
utilization, and c) less intuitive hyperparameter settings. To address these
limitations, we present \textit{NeuroKinect}, an innovative deep-learning model
for accurate reconstruction of hand kinematics using electroencephalography
(EEG) signals. \textit{NeuroKinect} model is trained on the Grasp and Lift
(GAL) tasks data with minimal preprocessing pipelines, subsequently improving
the computational efficiency. A notable improvement introduced by
\textit{NeuroKinect} is the utilization of a novel loss function, denoted as
. This loss function addresses the discrepancy
between correlation and mean square error in hand kinematics prediction.
Furthermore, our study emphasizes the scientific intuition behind parameter
selection to enhance accuracy. We analyze the spatial and temporal dynamics of
the motor movement task by employing event-related potential and brain source
localization (BSL) results. This approach provides valuable insights into the
optimal parameter selection, improving the overall performance and accuracy of
the \textit{NeuroKinect} model. Our model demonstrates strong correlations
between predicted and actual hand movements, with mean Pearson correlation
coefficients of 0.92 (0.015), 0.93 (0.019), and 0.83 (0.018) for
the X, Y, and Z dimensions. The precision of \textit{NeuroKinect} is evidenced
by low mean squared errors (MSE) of 0.016 (0.001), 0.015 (0.002), and
0.017 (0.005) for the X, Y, and Z dimensions, respectively
EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network
Motor kinematics decoding (MKD) using brain signal is essential to develop
Brain-computer interface (BCI) system for rehabilitation or prosthesis devices.
Surface electroencephalogram (EEG) signal has been widely utilized for MKD.
However, kinematic decoding from cortical sources is sparsely explored. In this
work, the feasibility of hand kinematics decoding using EEG cortical source
signals has been explored for grasp and lift task. In particular, pre-movement
EEG segment is utilized. A residual convolutional neural network (CNN) - long
short-term memory (LSTM) based kinematics decoding model is proposed that
utilizes motor neural information present in pre-movement brain activity.
Various EEG windows at 50 ms prior to movement onset, are utilized for hand
kinematics decoding. Correlation value (CV) between actual and predicted hand
kinematics is utilized as performance metric for source and sensor domain. The
performance of the proposed deep learning model is compared in sensor and
source domain. The results demonstrate the viability of hand kinematics
decoding using pre-movement EEG cortical source data
Unsupervised decoding of long-term, naturalistic human neural recordings with automated video and audio annotations
Fully automated decoding of human activities and intentions from direct
neural recordings is a tantalizing challenge in brain-computer interfacing.
Most ongoing efforts have focused on training decoders on specific, stereotyped
tasks in laboratory settings. Implementing brain-computer interfaces (BCIs) in
natural settings requires adaptive strategies and scalable algorithms that
require minimal supervision. Here we propose an unsupervised approach to
decoding neural states from human brain recordings acquired in a naturalistic
context. We demonstrate our approach on continuous long-term
electrocorticographic (ECoG) data recorded over many days from the brain
surface of subjects in a hospital room, with simultaneous audio and video
recordings. We first discovered clusters in high-dimensional ECoG recordings
and then annotated coherent clusters using speech and movement labels extracted
automatically from audio and video recordings. To our knowledge, this
represents the first time techniques from computer vision and speech processing
have been used for natural ECoG decoding. Our results show that our
unsupervised approach can discover distinct behaviors from ECoG data, including
moving, speaking and resting. We verify the accuracy of our approach by
comparing to manual annotations. Projecting the discovered cluster centers back
onto the brain, this technique opens the door to automated functional brain
mapping in natural settings
Noninvasive neural decoding of overt and covert hand movement
It is generally assumed that the signal-to-noise ratio and information content of neural data acquired noninvasively via magnetoencephalography (MEG) or scalp electroencephalography (EEG) are insufficient to extract detailed information about natural, multi-joint movements of the upper limb. If valid, this assumption could severely limit the practical usage of noninvasive signals in brain-computer interface (BCI) systems aimed at continuous complex control of arm-like prostheses for movement impaired persons. Fortunately this dissertation research casts doubt on the veracity of this assumption by extracting continuous hand kinematics from MEG signals collected during a 2D center-out drawing task (Bradberry et al. 2009, NeuroImage, 47:1691-700) and from EEG signals collected during a 3D center-out reaching task (Bradberry et al. 2010, Journal of Neuroscience, 30:3432-7). In both studies, multiple regression was performed to find a matrix that mapped past and current neural data from multiple sensors to current hand kinematic data (velocity). A novel method was subsequently devised that incorporated the weights of the mapping matrix and the standardized low resolution electromagnetic tomography (sLORETA) software to reveal that the brain sources that encoded hand kinematics in the MEG and EEG studies were corroborated by more traditional studies that required averaging across trials and/or subjects. Encouraged by the favorable results of these off-line decoding studies, a BCI system was developed for on-line decoding of covert movement intentions that provided users with real-time visual feedback of the decoder output. Users were asked to use only their thoughts to move a cursor to acquire one of four targets on a computer screen. With only one training session, subjects were able to accomplish this task. The promising results of this dissertation research significantly advance the state-of-the-art in noninvasive BCI systems
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