6 research outputs found
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
Novel Time Domain Based Upper-Limb Prosthesis Control using Incremental Learning Approach
The upper limb of the body is a vital for various kind of activities for
human. The complete or partial loss of the upper limb would lead to a
significant impact on daily activities of the amputees. EMG carries important
information of human physique which helps to decode the various functionalities
of human arm. EMG signal based bionics and prosthesis have gained huge research
attention over the past decade. Conventional EMG-PR based prosthesis struggles
to give accurate performance due to off-line training used and incapability to
compensate for electrode position shift and change in arm position. This work
proposes online training and incremental learning based system for upper limb
prosthetic application. This system consists of ADS1298 as AFE (analog front
end) and a 32 bit arm cortex-m4 processor for DSP (digital signal processing).
The system has been tested for both intact and amputated subjects. Time
derivative moment based features have been implemented and utilized for
effective pattern classification. Initially, system have been trained for four
classes using the on-line training process later on the number of classes have
been incremented on user demand till eleven, and system performance has been
evaluated. The system yielded a completion rate of 100% for healthy and
amputated subjects when four motions have been considered. Further 94.33% and
92% completion rate have been showcased by the system when the number of
classes increased to eleven for healthy and amputees respectively. The motion
efficacy test is also evaluated for all the subjects. The highest efficacy rate
of 91.23% and 88.64% are observed for intact and amputated subjects
respectively.Comment: 15 Pages, 8 Figures, This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notice, after
which this version may no longer be accessibl
Time and frequency domain analysis of physiological features during autonomic dysreflexia after spinal cord injury
IntroductionAutonomic dysreflexia (AD) affects about 70% of individuals with spinal cord injury (SCI) and can have severe consequences, including death if not promptly detected and managed. The current gold standard for AD detection involves continuous blood pressure monitoring, which can be inconvenient. Therefore, a non-invasive detection device would be valuable for rapid and continuous AD detection.MethodsImplanted rodent models were used to analyze autonomic dysreflexia after spinal cord injury. Skin nerve activity (SKNA) features were extracted from ECG signals recorded non-invasively, using ECG electrodes. At the same time, blood pressure and ECG data sampled was collected using an implanted telemetry device. Heart rate variability (HRV) features were extracted from these ECG signals. SKNA and HRV parameters were analyzed in both the time and frequency domain.ResultsWe found that SKNA features showed an increase approximately 18 seconds before the typical rise in systolic blood pressure, indicating the onset of AD in a rat model with upper thoracic SCI. Additionally, low-frequency components of SKNA in the frequency domain were dominant during AD, suggesting their potential inclusion in an AD detection system for improved accuracy.DiscussionUtilizing SKNA measurements could enable early alerts to individuals with SCI, allowing timely intervention and mitigation of the adverse effects of AD, thereby enhancing their overall well-being and safety
Advancing spinal cord injury care through non-invasive autonomic dysreflexia detection with AI
Abstract This paper presents an AI-powered solution for detecting and monitoring Autonomic Dysreflexia (AD) in individuals with spinal cord injuries. Current AD detection methods are limited, lacking non-invasive monitoring systems. We propose a model that combines skin nerve activity (SKNA) signals with a deep neural network (DNN) architecture to overcome this limitation. The DNN is trained on a meticulously curated dataset obtained through controlled colorectal distension, inducing AD events in rats with spinal cord surgery above the T6 level. The proposed system achieves an impressive average classification accuracy of 93.9% ± 2.5%, ensuring accurate AD identification with high precision (95.2% ± 2.1%). It demonstrates a balanced performance with an average F1 score of 94.4% ± 1.8%, indicating a harmonious balance between precision and recall. Additionally, the system exhibits a low average false-negative rate of 4.8% ± 1.6%, minimizing the misclassification of non-AD cases. The robustness and generalizability of the system are validated on unseen data, maintaining high accuracy, F1 score, and a low false-negative rate. This AI-powered solution represents a significant advancement in non-invasive, real-time AD monitoring, with the potential to improve patient outcomes and enhance AD management in individuals with spinal cord injuries. This research contributes a promising solution to the critical healthcare challenge of AD detection and monitoring
Optimizing bio-sensor design with support vector regression technique for AlGaN/GaN MOS-HEMT
This letter introduces a novel approach using support vector regression (SVR) for sensitivity modeling of gallium nitride (GaN) metal oxide semiconductor (MOS)–high electron mobility transistors (HEMTs). By combining experimental and simulation results, the SVR-based model is developed to predict sensitivities. The fabricated AlGaN/GaN HEMTs incorporate a graded transition scheme, a 1-nm AlN spacer, 2-nm GaN cap layer, and 10-nm Al 2 O 3 as the gate dielectric/sensing layer. To train the model, feature matrices are prepared using pH sensing results from 32 device dimensional variants. The trained model is then used to predict sensitivities for other device dimensions, allowing for device design optimization and exploration of the design space. Among the five considered kernels (linear, cubic, fine Gaussian, medium Gaussian, and coarse Gaussian), the quadratic-kernel-based SVR demonstrates the best performance, yielding a root mean square (RMS) error of 0.1767 and a standard deviation of 0.0654