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Add to EdgeInsights into Age-Related Functional Brain Changes during Audiovisual Integration Tasks: A Comprehensive EEG Source-Based Analysis
Nov 27, 2023
The seamless integration of visual and auditory information is a fundamental aspect of human cognition. Although age-related functional changes in Audio-Visual Integration (AVI) have been extensively explored in the past, thorough studies across various age groups remain insufficient. Previous studies have provided valuable insights into agerelated AVI using EEG-based sensor data. However, these studies have been limited in their ability to capture spatial information related to brain source activation and their connectivity. To address these gaps, our study conducted a comprehensive audiovisual integration task with a specific focus on assessing the aging effects in various age groups, particularly middle-aged individuals. We presented visual, auditory, and audio-visual stimuli and recorded EEG data from Young (18-25 years), Transition (26- 33 years), and Middle (34-42 years) age cohort healthy participants. We aimed to understand how aging affects brain activation and functional connectivity among hubs during audio-visual tasks. Our findings revealed delayed brain activation in middleaged individuals, especially for bimodal stimuli. The superior temporal cortex and superior frontal gyrus showed significant changes in neuronal activation with aging. Lower frequency bands (theta and alpha) showed substantial changes with increasing age during AVI. Our findings also revealed that the AVI-associated brain regions can be clustered into five different brain networks using the k-means algorithm. Additionally, we observed increased functional connectivity in middle age, particularly in the frontal, temporal, and occipital regions. These results highlight the compensatory neural mechanisms involved in aging during cognitive tasks.
Modeling and parametric optimization of 3D tendon-sheath actuator system for upper limb soft exosuit
Jun 30, 2023
This paper presents an analysis of parametric characterization of a motor driven tendon-sheath actuator system for use in upper limb augmentation for applications such as rehabilitation, therapy, and industrial automation. The double tendon sheath system, which uses two sets of cables (agonist and antagonist side) guided through a sheath, is considered to produce smooth and natural-looking movements of the arm. The exoskeleton is equipped with a single motor capable of controlling both the flexion and extension motions. One of the key challenges in the implementation of a double tendon sheath system is the possibility of slack in the tendon, which can impact the overall performance of the system. To address this issue, a robust mathematical model is developed and a comprehensive parametric study is carried out to determine the most effective strategies for overcoming the problem of slack and improving the transmission. The study suggests that incorporating a series spring into the system's tendon leads to a universally applicable design, eliminating the need for individual customization. The results also show that the slack in the tendon can be effectively controlled by changing the pretension, spring constant, and size and geometry of spool mounted on the axle of motor.
Brain Connectivity Features-based Age Group Classification using Temporal Asynchrony Audio-Visual Integration Task
May 01, 2023
The process of integration of inputs from several sensory modalities in the human brain is referred to as multisensory integration. Age-related cognitive decline leads to a loss in the ability of the brain to conceive multisensory inputs. There has been considerable work done in the study of such cognitive changes for the old age groups. However, in the case of middle age groups, such analysis is limited. Motivated by this, in the current work, EEG-based functional connectivity during audiovisual temporal asynchrony integration task for middle-aged groups is explored. Investigation has been carried out during different tasks such as: unimodal audio, unimodal visual, and variations of audio-visual stimulus. A correlation-based functional connectivity analysis is done, and the changes among different age groups including: young (18-25 years), transition from young to middle age (25-33 years), and medium (33-41 years), are observed. Furthermore, features extracted from the connectivity graphs have been used to classify among the different age groups. Classification accuracies of $89.4\%$ and $88.4\%$ are obtained for the Audio and Audio-50-Visual stimuli cases with a Random Forest based classifier, thereby validating the efficacy of the proposed method.
EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network
Apr 13, 2023
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.
BiCurNet: Pre-Movement EEG based Neural Decoder for Biceps Curl Trajectory Estimation
Jan 10, 2023
Kinematic parameter (KP) estimation from early electroencephalogram (EEG) signals is essential for positive augmentation using wearable robot. However, work related to early estimation of KPs from surface EEG is sparse. In this work, a deep learning-based model, BiCurNet, is presented for early estimation of biceps curl using collected EEG signal. The model utilizes light-weight architecture with depth-wise separable convolution layers and customized attention module. The feasibility of early estimation of KPs is demonstrated using brain source imaging. Computationally efficient EEG features in spherical and head harmonics domain is utilized for the first time for KP prediction. The best Pearson correlation coefficient (PCC) between estimated and actual trajectory of $0.7$ is achieved when combined EEG features (spatial and harmonics domain) in delta band is utilized. Robustness of the proposed network is demonstrated for subject-dependent and subject-independent training, using EEG signals with artifacts.
Subject-Independent 3D Hand Kinematics Reconstruction using Pre-Movement EEG Signals for Grasp And Lift Task
Sep 05, 2022
Brain-computer interface (BCI) systems can be utilized for kinematics decoding from scalp brain activation to control rehabilitation or power-augmenting devices. In this study, the hand kinematics decoding for grasp and lift task is performed in three-dimensional (3D) space using scalp electroencephalogram (EEG) signals. Twelve subjects from the publicly available database WAY-EEG-GAL, has been utilized in this study. In particular, multi-layer perceptron (MLP) and convolutional neural network-long short-term memory (CNN-LSTM) based deep learning frameworks are proposed that utilize the motor-neural information encoded in the pre-movement EEG data. Spectral features are analyzed for hand kinematics decoding using EEG data filtered in seven frequency ranges. The best performing frequency band spectral features has been considered for further analysis with different EEG window sizes and lag windows. Appropriate lag windows from movement onset, make the approach pre-movement in true sense. Additionally, inter-subject hand trajectory decoding analysis is performed using leave-one-subject-out (LOSO) approach. The Pearson correlation coefficient and hand trajectory are considered as performance metric to evaluate decoding performance for the neural decoders. This study explores the feasibility of inter-subject 3-D hand trajectory decoding using EEG signals only during reach and grasp task, probably for the first time. The results may provide the viable information to decode 3D hand kinematics using pre-movement EEG signals for practical BCI applications such as exoskeleton/exosuit and prosthesis.
Reorganization of resting state brain network functional connectivity across human brain developmental stages
Jun 16, 2022
The human brain is liable to undergo substantial alterations, anatomically and functionally with aging. Cognitive brain aging can either be healthy or degenerative in nature. Such degeneration of cognitive ability can lead to disorders such as Alzheimer's disease, dementia, schizophrenia, and multiple sclerosis. Furthermore, the brain network goes through various changes during healthy aging, and it is an active area of research. In this study, we have investigated the rs-functional connectivity of participants (in the age group of 7-89 years) using a publicly available HCP dataset. We have also explored how different brain networks are clustered using K-means clustering methods which have been further validated by the t-SNE algorithm. The changes in overall resting-state brain functional connectivity with changes in brain developmental stages have also been explored using BrainNet Viewer. Then, specifically within-cluster network and between-cluster network changes with increasing age have been studied using linear regression which ultimately shows a pattern of increase/decrease in the mean segregation of brain networks with healthy aging. Brain networks like Default Mode Network, Cingulo opercular Network, Sensory Motor Network, and Cerebellum Network have shown decreased segregation whereas Frontal Parietal Network and Occipital Network show increased segregation with healthy aging. Our results strongly suggest that the brain has four brain developmental stages and brain networks reorganize their functional connectivity during these brain developmental stages.
PreMovNet: Pre-Movement EEG-based Hand Kinematics Estimation for Grasp and Lift task
May 02, 2022
Kinematics decoding from brain activity helps in developing rehabilitation or power-augmenting brain-computer interface devices. Low-frequency signals recorded from non-invasive electroencephalography (EEG) are associated with the neural motor correlation utilised for motor trajectory decoding (MTD). In this communication, the ability to decode motor kinematics trajectory from pre-movement delta-band (0.5-3 Hz) EEG is investigated for the healthy participants. In particular, two deep learning-based neural decoders called PreMovNet-I and PreMovNet-II, are proposed that make use of motor-related neural information existing in the pre-movement EEG data. EEG data segments with various time lags of 150 ms, 200 ms, 250 ms, 300 ms, and 350 ms before the movement onset are utilised for the same. The MTD is presented for grasp-and-lift task (WAY-EEG-GAL dataset) using EEG with the various lags taken as input to the neural decoders. The performance of the proposed decoders are compared with the state-of-the-art multi-variable linear regression (mLR) model. Pearson correlation coefficient and hand trajectory are utilised as performance metric. The results demonstrate the viability of decoding 3D hand kinematics using pre-movement EEG data, enabling better control of BCI-based external devices such as exoskeleton/exosuit.
Adaptive area-preserving parameterization of open and closed anatomical surfaces
Nov 08, 2021
The parameterization of open and closed anatomical surfaces is of fundamental importance in many biomedical applications. Spherical harmonics, a set of basis functions defined on the unit sphere, are widely used for anatomical shape description. However, establishing a one-to-one correspondence between the object surface and the entire unit sphere may induce a large geometric distortion in case the shape of the surface is too different from a perfect sphere. In this work, we propose adaptive area-preserving parameterization methods for simply-connected open and closed surfaces with the target of the parameterization being a spherical cap. Our methods optimize the shape of the parameter domain along with the mapping from the object surface to the parameter domain. The object surface will be globally mapped to an optimal spherical cap region of the unit sphere in an area-preserving manner while also exhibiting low conformal distortion. We further develop a set of spherical harmonics-like basis functions defined over the adaptive spherical cap domain, which we call the adaptive harmonics. Experimental results show that the proposed parameterization methods outperform the existing methods for both open and closed anatomical surfaces in terms of area and angle distortion. Surface description of the object surfaces can be effectively achieved using a novel combination of the adaptive parameterization and the adaptive harmonics. Our work provides a novel way of mapping anatomical surfaces with improved accuracy and greater flexibility. More broadly, the idea of using an adaptive parameter domain allows easy handling of a wide range of biomedical shapes.
Source Aware Deep Learning Framework for Hand Kinematic Reconstruction using EEG Signal
Apr 05, 2021
The ability to reconstruct the kinematic parameters of hand movement using non-invasive electroencephalography (EEG) is essential for strength and endurance augmentation using exosuit/exoskeleton. For system development, the conventional classification based brain computer interface (BCI) controls external devices by providing discrete control signals to the actuator. A continuous kinematic reconstruction from EEG signal is better suited for practical BCI applications. The state-of-the-art multi-variable linear regression (mLR) method provides a continuous estimate of hand kinematics, achieving maximum correlation of upto 0.67 between the measured and the estimated hand trajectory. In this work, three novel source aware deep learning models are proposed for motion trajectory prediction (MTP). In particular, multi layer perceptron (MLP), convolutional neural network - long short term memory (CNN-LSTM), and wavelet packet decomposition (WPD) CNN-LSTM are presented. Additional novelty of the work includes utilization of brain source localization (using sLORETA) for the reliable decoding of motor intention mapping (channel selection) and accurate EEG time segment selection. Performance of the proposed models are compared with the traditionally utilised mLR technique on the real grasp and lift (GAL) dataset. Effectiveness of the proposed framework is established using the Pearson correlation coefficient and trajectory analysis. A significant improvement in the correlation coefficient is observed when compared with state-of-the-art mLR model. Our work bridges the gap between the control and the actuator block, enabling real time BCI implementation.