Refined Force Estimation in Monkey's Pinching Tasks Through Integrated EMG and ECoG Data: A Kalman Filter Method.

Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

Fully automated decoding of human activities and intentions from direct neural recordings is a tantalizing challenge in brain-computer interfacing. Implementing Brain Computer Interfaces (BCIs) outside carefully controlled experiments in laboratory settings requires adaptive and scalable strategies with minimal supervision. Here we describe an unsupervised approach to decoding neural states from naturalistic human brain recordings. We analyzed continuous, long-term electrocorticography (ECoG) data recorded over many days from the brain of subjects in a hospital room, with simultaneous audio and video recordings. We discovered coherent clusters in high-dimensional ECoG recordings using hierarchical clustering and automatically annotated them using speech and movement labels extracted from audio and video. To our knowledge, this represents the first time techniques from computer vision and speech processing have been used for natural ECoG decoding. Interpretable behaviors were decoded from ECoG data, including moving, speaking and resting; the results were assessed by comparison with manual annotation. Discovered clusters were projected back onto the brain revealing features consistent with known functional areas, opening the door to automated functional brain mapping in natural settings.

Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.

Motor Brain-Computer Interfaces (BCIs) are systems that allow severely motor-impaired patients to use their brain activity to interact with their environment. Electrocorticography (ECoG) arrays may be profitably used to develop safe and chronic motor BCI systems. BCI signal processing pipelines generally include neuronal signal pre-processing, feature extraction and classification/regression. The article presents a comparative study addressing the problem of neural feature classification in asynchronous multi-limb ECoG-driven BCIs. Several conventional classifiers often reported in the BCI literature were coupled with two preprocessing techniques and with a conventional feature extraction approach. They were compared to artificial neural network (ANN) end-to-end classifiers which mimic conventional BCI signal processing pipelines. Different initializations of ANNs were particularly studied. The comparison study was carried out using publicly available datasets (BCI competition IV).

BACKGROUND Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor impairments, including tremors, rigidity, and bradykinesia. These symptoms significantly affect patients' daily activities and quality of life. Current treatment options, such as medication and deep brain stimulation (DBS), have limitations, including side effects and high costs. Therefore, there is a need for an alternative, non-invasive assistive solution to improve motor function in Parkinson’s patients. OBJECTIVE The objective of this study is to develop a brain-body interface that utilizes EEG, EMG, and FES to assist Parkinson’s patients in controlling their motor movements. By synchronizing neural and muscular signals, the system aims to facilitate voluntary movement and reduce tremors without invasive procedures. This research seeks to establish a theoretical model for signal processing and movement generation, forming the foundation for future prototype development and clinical validation. METHODS This section provides a detailed explanation of the methodology used in developing the Parkinson’s assistive device. The approach involves multiple components, each playing a crucial role in capturing, processing, and responding to neurological and muscular signals to facilitate controlled movement. i. EEG Sensor Electroencephalography (EEG) sensors are used to capture brain signals, specifically detecting neural activity associated with movement intention. The EEG data is processed using signal processing algorithms to extract relevant patterns that indicate the user's intent to move a specific muscle group. ii. EMG Sensor Electromyography (EMG) sensors detect electrical activity in muscles. These sensors help in monitoring voluntary and involuntary muscle contractions. By integrating EEG and EMG data, the system enhances the accuracy of movement prediction and stimulation. iii. Functional Electrical Stimulation (FES) FES is used to generate electrical impulses that stimulate specific muscles, facilitating movement in patients experiencing tremors or rigidity. The FES unit receives processed signals from the STM32 microcontroller, ensuring precise and controlled stimulation. iv. STM32 Microcontroller The STM32 microcontroller serves as the central processing unit, responsible for handling signals from EEG and EMG sensors, processing them using machine learning algorithms, and sending appropriate stimulation signals to the FES system. It ensures real-time synchronization between brain activity, muscle response, and electrical stimulation. v. Battery System The device is powered by a rechargeable battery system, providing a stable and efficient energy supply. Power management circuits are implemented to optimize energy consumption, ensuring long-term usability without frequent recharging. vi. Signal Processing and Data Flow  EEG signals are collected and filtered to remove noise.  EMG signals are simultaneously captured to correlate neural activity with muscle activity.  The STM32 microcontroller processes these signals and applies machine learning models to predict intended movement.  The microcontroller sends precise electrical stimulation commands to the FES unit.  The FES unit stimulates the target muscles, enabling controlled movement. vii. Synchronization and Feedback Mechanism To improve accuracy, a feedback loop is implemented where real-time responses from the muscles (EMG) are re-evaluated, and adjustments are made dynamically to the stimulation parameters. This ensures adaptive and efficient motor control. RESULTS In this study, a theoretical model was developed to integrate EEG, EBG, and EMG signals for effective control of Functional Electrical Stimulation (FES) in Parkinson’s patients. The preliminary analysis of signal synchronization and processing suggests that this approach has the potential to facilitate controlled motor movements. 1. Theoretical Validation: The signal processing framework was designed based on existing neurophysiological principles. The expected interactions between EEG and EMG signals indicate that FES can be triggered appropriately to induce movement. 2. Expected Outcomes: The anticipated result of this system is an improvement in motor function for Parkinson’s patients by translating neural intent into physical action. The model predicts that synchronized stimulation can assist in reducing tremors and enhancing voluntary movements. 3. Future Work: The next phase involves developing a prototype for real-world testing. Experimental validation through hardware implementation will be conducted to confirm the effectiveness of the proposed system. CONCLUSIONS This research presents a significant advancement in assistive technology for individuals with Parkinson’s disease. By integrating EEG, EMG, and FES sensors with an STM32 microcontroller, the system effectively interprets neural and muscular signals to generate precise stimulation, aiding in movement control. The study highlights the potential of electrical stimulation and brain-computer interface technology in enhancing motor functions without invasive procedures. The development of this device marks a step forward in neuro-assistive solutions, providing a non-invasive, adaptive, and user-friendly system for patients. The integration of AI-driven signal processing and real-time data adaptation further enhances the system’s efficiency and accuracy. The miniaturization of components and transition to wearable technology will improve usability and accessibility for daily life applications. Future improvements will focus on optimizing the device’s performance, expanding its application to other neurological disorders, and conducting extensive clinical trials to validate its effectiveness. Through continuous innovation and collaboration with healthcare professionals, this technology has the potential to revolutionize treatment approaches for movement impairments, offering a better quality of life for affected individuals. CLINICALTRIAL

Decoding micro-electrocorticographic signals by using explainable 3D convolutional neural network to predict finger movements

This study aimed to design a neural interface that extracts movement commands from the brain to generate appropriate intra-spinal stimulation to restore leg movement. This study comprised four steps: (1) Recording electrocorticographic (ECoG) signals and corresponding leg movements in different trials. (2) Partial laminectomy to induce spinal cord injury (SCI) and detect motor modules in the spinal cord. (3) Delivering appropriate intra-spinal stimulation to the motor modules for restoration of the movements to those documented before SCI. (4) Development of a neural interface created by sparse linear regression (SLiR) model to detect movement commands transmitted from the brain to the modules. Correlation coefficient (CC) and normalized root mean square (NRMS) error was calculated to evaluate the neural interface effectiveness. It was found that by stimulating detected spinal cord modules, joint angle evaluated before SCI was not significantly different from that of post-SCI (P > 0.05). Based on results of SLiR model, overall CC and NRMS values were 0.63 ± 0.14 and 0.34 ± 0.16 (mean ± SD), respectively. These results indicated that ECoG data contained information about intra-spinal stimulations and the developed neural interface could produce intra-spinal stimulation based on ECoG data, for restoration of leg movements after SCI.

Low delay connection strength estimation of time-variant neuronal network with Kalman filter

Background: In this study, different intent prediction strategies were explored with the objective of determining the best approach to predicting continuous multi-axial user motion based solely on surface EMG (electromyography) data. These strategies were explored as the first step to better facilitating control of a multi-axis transtibial powered prosthesis.Methods: Based on data acquired from gait experiments, different data sets, prediction approaches and classification algorithms were explored. The effect of varying EMG electrode positioning was also tested. EMG data measured from three lower leg muscles was the sole data type used for making intent predictions. The motions to be predicted were along both the sagittal plane (foot dorsiflexion and plantarflexion) and the frontal plane (foot eversion and inversion).Results: The deviation of EMG data from its optimal pattern led to a decrease in prediction accuracy of up to 23%. However, using features that were calculated based on a participant's specific walking pattern limited this loss of prediction accuracy as a result of EMG electrode placement. A decoupled data set, one wherein the terrain type was accounted for beforehand, yielded the highest intent prediction accuracy of 77.2%.Conclusions: The results of this study highlighted the challenges faced when using very limited EMG data to predict multi-axial ankle motion. They also indicated that approaches that are more user-centric by design could led to more accurate motion predictions, possibly enabling more intuitive control.

T101. Use of a quantitative algorithm to help predict seizure lateralization in a patient with bitemporal epilepsy and responsive nerve stimulation

Reaction time is a surrogate measure of alertness and fatigue, which is essential to brain-computer interface (BCI) research in understanding and preventing fatigue in driving. Accurate reaction time determination is crucial, but complicated by the continuous nature of steering wheel inputs. Continuous data requires the selection of thresholds to determine valid reactions, and such thresholds may be under-sensitive or over-sensitive. In this study, steering wheel and electromyography (EMG) data collected from a BCI driving fatigue experiment was analyzed to determine both mechanical movement onset and EMG activation times. Several automatic methods for detecting wheel movement onset were evaluated against manual detection by experimenters. Results showed that single threshold detection with output smoothing yielded nearly identical results, whilst allowing for automatic determination of erroneous trials. EMG data analysis also showed that activation of the triceps long head muscles consistently preceded mechanical movement onset by 0.1s. These results collectively demonstrate that steering reaction time can be accurately determined through automatic methods, and that EMG activation data can be used as verification or a substitute for mechanically determined reaction times, thereby laying the groundwork for further BCI research into driving fatigue.

It has not been established how electromyographic (EMG) data of masticatory muscles can estimate bite force (BF) during daily activities at home, such as eating and bruxism, utilising the EMG-BF correlation. This study aimed to investigate the relationship between actual BF and BF estimated using corresponding EMG data and additional information on BF and EMG measured on a separate day. Participants were 16 volunteers. The unilateral masseteric EMG was recorded during clenching tasks at 10 levels of force up to maximum voluntary clenching (MVC) twice on separate days (Day 1, Day 2). BF was simultaneously measured using a pressure-sensitive occlusal film. The regression equation between the BF and EMG amplitude was calculated on Day 1. Estimated BF on Day 2 was calculated using information on the EMG amplitude on Day 2 (EMG-amp), Day 1 BF at the MVC, and Day 1 regression equation. Actual value of BF showed a small correlation coefficient with EMG-amp, whereas strong correlations were observed with the estimated values additionally using information of Day 1 BF at the MVC. The estimated BF additionally using information of Day 1 regression equation adjusted by the ratio of EMG at the MVC on Day 1 to that of Day 2 showed the smallest error, indicating the power to estimate BF using corresponding EMG data became further improved. The obtained findings suggest the possibility of the clinical method estimating BF using corresponding EMG data with the additional information of EMG and BF on a separate day.

Objective. The purpose of this study was to determine the contribution of electromyography (EMG) data, in combination with a diverse array of mechanical sensors, to locomotion mode intent recognition in transfemoral amputees using powered prostheses. Additionally, we determined the effect of adding time history information using a dynamic Bayesian network (DBN) for both the mechanical and EMG sensors. Approach. EMG signals from the residual limbs of amputees have been proposed to enhance pattern recognition‐based intent recognition systems for powered lower limb prostheses, but mechanical sensors on the prosthesis—such as inertial measurement units, position and velocity sensors, and load cells—may be just as useful. EMG and mechanical sensor data were collected from 8 transfemoral amputees using a powered knee/ankle prosthesis over basic locomotion modes such as walking, slopes and stairs. An offline study was conducted to determine the benefit of different sensor sets for predicting intent. Main results. EMG information was not as accurate alone as mechanical sensor information (p < 0.05) for any classification strategy. However, EMG in combination with the mechanical sensor data did significantly reduce intent recognition errors (p < 0.05) both for transitions between locomotion modes and steady-state locomotion. The sensor time history (DBN) classifier significantly reduced error rates compared to a linear discriminant classifier for steady-state steps, without increasing the transitional error, for both EMG and mechanical sensors. Combining EMG and mechanical sensor data with sensor time history reduced the average transitional error from 18.4% to 12.2% and the average steady-state error from 3.8% to 1.0% when classifying level-ground walking, ramps, and stairs in eight transfemoral amputee subjects. Significance. These results suggest that a neural interface in combination with time history methods for locomotion mode classification can enhance intent recognition performance; this strategy should be considered for future real-time experiments.

Like many complex dynamic systems, the brain exhibits scale-free dynamics that follow power-law scaling. Broadband power spectral density (PSD) of brain electrical activity exhibits state-dependent power-law scaling with a log frequency exponent that varies across frequency ranges. Widely divergent naturally occurring neural states, awake and slow wave sleep (SWS), were used to evaluate the nature of changes in scale-free indices of brain electrical activity. We demonstrate two analytic approaches to characterizing electrocorticographic (ECoG) data obtained during awake and SWS states. A data-driven approach was used, characterizing all available frequency ranges. Using an equal error state discriminator (EESD), a single frequency range did not best characterize state across data from all six subjects, though the ability to distinguish awake and SWS ECoG data in individual subjects was excellent. Multi-segment piecewise linear fits were used to characterize scale-free slopes across the entire frequency range (0.2–200 Hz). These scale-free slopes differed between awake and SWS states across subjects, particularly at frequencies below 10 Hz and showed little difference at frequencies above 70 Hz. A multivariate maximum likelihood analysis (MMLA) method using the multi-segment slope indices successfully categorized ECoG data in most subjects, though individual variation was seen. In exploring the differences between awake and SWS ECoG data, these analytic techniques show that no change in a single frequency range best characterizes differences between these two divergent biological states. With increasing computational tractability, the use of scale-free slope values to characterize ECoG and EEG data will have practical value in clinical and research studies.

This study presents a preliminary analysis of the relationship between electroencephalographic (EEG) and electrocorticographic (ECoG) event-related potentials (ERPs) recorded from from a single patient using a brain-computer interface (BCI) speller. The patient had medically intractable epilepsy and underwent temporary placement of an intracranial ECoG grid electrode array to localize seizure foci. The patient performed one experimental session using the BCI spelling paradigm controlled by scalp-recorded EEG prior to the ECoG grid implantation, and one identical session controlled by ECoG after the grid implantation. The patient was able to achieve near perfect spelling accuracy using EEG and ECoG. An offline analysis of the average ERPs was performed to assess how accurately the average EEG ERPs could be predicted from the ECoG data. The preliminary results indicate that EEG ERPs can be accurately estimated from proximal asynchronous ECoG data using simple linear spatial models.