Systemic Review on Transcranial Electrical Stimulation Parameters and EEG/fNIRS Features for Brain Diseases

Frontiers in Neuroscience, Journal Year: 2021, Volume and Issue: 15

Published: March 26, 2021

Brain disorders are gradually becoming the leading cause of death worldwide. However, lack knowledge brain disease's underlying mechanisms and ineffective neuropharmacological therapy have led to further exploration optimal treatments monitoring techniques.This study aims review current state disorders, which utilize transcranial electrical stimulation (tES) daily usable noninvasive neuroimaging techniques. Furthermore, second goal this is highlight available gaps provide a comprehensive guideline for investigation.A systematic search was conducted PubMed Web Science databases from January 2000 October 2020 using relevant keywords. Electroencephalography (EEG) functional near-infrared spectroscopy were selected as modalities. Nine investigated in study, including Alzheimer's disease, depression, autism spectrum disorder, attention-deficit hyperactivity epilepsy, Parkinson's stroke, schizophrenia, traumatic injury.Sixty-seven studies (1,385 participants) included quantitative analysis. Most articles (82.6%) employed direct an intervention method with modulation parameters 1 mA intensity (47.2%) 16-20 min (69.0%) duration single session (36.8%). The frontal cortex (46.4%) cerebral (47.8%) used modality, power (45.7%) commonly extracted EEG feature.An appropriate protocol applying tES could be effective treatment cognitive neurological disorders. criteria not been defined; they vary across persons disease types. Therefore, future work needs investigate closed-loop by techniques achieve personalized

Language: Английский

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RobHand: A Hand Exoskeleton With Real-Time EMG-Driven Embedded Control. Quantifying Hand Gesture Recognition Delays for Bilateral Rehabilitation

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 137809 - 137823

Published: Jan. 1, 2021

Assisted bilateral rehabilitation has been proven to help patients improve their paretic limb ability and promote motor recovery, especially in upper limbs, after suffering a cerebrovascular accident (ACV). Robotic-assisted based on sEMG-driven control previously addressed other studies hand mobility; however, low-cost embedded solutions for the real-time bio-cooperative of robotic platforms are lacking. This paper presents RobHand (Robot Hand Rehabilitation) system, which is an exoskeleton that supports EMG-driven assisted by using custom-made EMG solution. A threshold non-pattern recognition developed, it detects gestures healthy replicates gesture placed hand. preliminary study with ten subjects conducted evaluate performance reliability, tracking accuracy response time proposed strategy solution, findings could be extrapolated stroke patients. systematic review carried out compare results study, present 97% overall detection indicate adequate responsiveness system.

Language: Английский

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A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application

Bioengineering, Journal Year: 2022, Volume and Issue: 9(12), P. 768 - 768

Published: Dec. 5, 2022

Patients with severe CNS injuries struggle primarily their sensorimotor function and communication the outside world. There is an urgent need for advanced neural rehabilitation intelligent interaction technology to provide help patients nerve injuries. Recent studies have established brain-computer interface (BCI) in order appropriate methods or more training. This paper reviews most recent research on brain-computer-interface-based non-invasive systems. Various endogenous exogenous methods, advantages, limitations, challenges are discussed proposed. In addition, discusses between various modes used severely paralyzed locked surrounding environment, particularly system utilizing (induced) EEG signals (such as P300 SSVEP). discussion reveals examination of collecting signals, components, signal postprocessing. Furthermore, describes development natural strategies, a focus acquisition, data processing, pattern recognition algorithms, control techniques.

Language: Английский

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Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal

IEEE Transactions on Cybernetics, Journal Year: 2022, Volume and Issue: 53(7), P. 4094 - 4106

Published: May 9, 2022

The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endurance augmentation exoskeleton/exosuit. For system development, conventional classification-based brain-computer interface (BCI) controls external devices by providing discrete control signals actuator. A continuous reconstruction from EEG signal better suited practical BCI applications. state-of-the-art multivariable linear regression (mLR) method provides a estimate kinematics, achieving maximum correlation up 0.67 between measured estimated trajectory. In this work, three novel source aware deep learning models are proposed motion trajectory prediction (MTP). particular, multilayer perceptron (MLP), convolutional neural network-long short-term memory (CNN-LSTM), wavelet packet decomposition (WPD) CNN-LSTM presented. addition, novelty in work includes utilization brain localization (BSL) [using standardized low-resolution electromagnetic tomography (sLORETA)] reliable decoding motor intention. information utilized channel selection accurate time segment selection. performance compared with traditionally mLR technique on reach, grasp, lift (GAL) dataset. effectiveness framework established Pearson coefficient (PCC) analysis. significant improvement observed when model. Our bridges gap actuator block, enabling real-time implementation.

Language: Английский

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Non-Invasive Human-Machine Interface (HMI) Systems With Hybrid On-Body Sensors for Controlling Upper-Limb Prosthesis: A Review

IEEE Sensors Journal, Journal Year: 2022, Volume and Issue: 22(11), P. 10292 - 10307

Published: April 22, 2022

In this work, we present a systematic review on non-invasive HMIs employing hybrid wearable sensor modalities for recognition of upper limb intentions. Different combinations the sensors are investigated. As sEMG is dominant in applications externally powered prosthetic hands, it involved most combinations. The combined use and IMU studied literature as easy to be integrated. Though limited, investigation other has been drawing more research attention efforts, especially those with FMG NIRS. For all reported sensors, verified that strategy can enrich information user intention help pattern and/or intensity regulation robotic hand/arm prosthesis. trend, development these hybrid-sensor-based still at preliminary stage. More dedicated fusion models system architectures well new features algorithms need developed make best each sensing modality's strength achieve robust stable recognition, which essential progress acceptance

Language: Английский

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Wearable and Comfortable e-Textile Headband for Long-Term Acquisition of Forehead EEG Signals

IEEE Sensors Journal, Journal Year: 2020, Volume and Issue: 20(24), P. 15107 - 15116

Published: July 16, 2020

Electroencephalography (EEG) has a wide range of applications in medical diagnosis, and novel form Human Machine Interfaces (HMI) for controlling prosthetic implants, wheelchairs, home appliances various forms paralysis. However, the current EEG setups are composed many wires hanging down from system, individual electrodes that must be set manually, which is time-consuming. As result, overall system neither comfortable, nor aesthetically appealing. Here, we introduce first time, comfortable textile-based headband soft, conformal to skin, comfortable. We present materials methods fabrication multi-layer stretchable e-textile, interfaces human epidermis one side through printed electrodes, rigid PCB island on second layer. as well demonstrate method allows creation VIAs (vertical interconnect access) between layers, using CO2 laser. All Electrodes integrated into headband, thus there no need electrode placement, wiring. By screen printing home-made conductive ink, patient-specific headbands can tailor made considering optimal positioning each patient. show these benefit very low skin-electrode impedance, comparable gold standard Ag/AgCl, or cup thanks high surface area silver flakes used this work. The e-textile with an acquisition device captures, amplifies, transmits data external mobile phone PC. Furthermore, amplification textile use EMF rejection layer top were shown reduce unwanted EM noise picked up by system. application developed usage Sleep Data Acquisition. Altogether, step toward wider devices daily-use applications.

Language: Английский

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Passive Brain-Computer Interfaces for Enhanced Human-Robot Interaction

Frontiers in Robotics and AI, Journal Year: 2020, Volume and Issue: 7

Published: Oct. 2, 2020

Brain-computer interfaces (BCIs) have long been seen as control that translate changes in brain activity, produced either by means of a volitional modulation or response to an external stimulation. However, recent trends the BCI and neurofeedback research highlight passive monitoring user's activity order estimate cognitive load, attention level, perceived errors emotions. Extraction such higher information from signals is gateway for facilitation interaction between humans intelligent systems. Particularly field robotics, BCIs provide promising channel prediction affective state development user-adaptive interaction. In this paper, we first illustrate art technology then examples employment human-robot (HRI). We finally discuss prospects challenges integration socially demanding HRI settings. This work intends inform community opportunities offered systems enhancement while recognizing potential pitfalls.

Language: Английский

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Exoskeletal Devices for Hand Assistance and Rehabilitation: A Comprehensive Analysis of State-of-the-Art Technologies

IEEE Transactions on Medical Robotics and Bionics, Journal Year: 2021, Volume and Issue: 3(2), P. 525 - 538

Published: March 8, 2021

Robots are effective tools for aiding in the restoration of hand function through rehabilitation programs or by providing in-task assistance. To date, a multitude exoskeletal devices employing distinct technologies have been proposed, making navigating this field challenging task. end, we propose set classification criteria to help categorize devices. In review, 97 publications representing 72 active assistance and is analysed. Furthermore, distribution over years within each presented. Results show clear trends, such as preferring underactuated devices, electrical transducers with flexible transmission more recent uptake soft technologies. Lastly, readiness level exoskeleton technology presented terms whole device identified sub-classifications. Most still laboratory testing phase, undergoing healthy subject trials limited clinical trials, very few having actually reached market. We hope provide researchers comprehensive analysis currently employed design choices exoskeletons, highlighting most developed avenues research latest emerging ones.

Language: Английский

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Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

Sensors, Journal Year: 2021, Volume and Issue: 21(20), P. 6863 - 6863

Published: Oct. 15, 2021

As a definition, Human-Machine Interface (HMI) enables person to interact with device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved way new class HMIs, which take such as inputs control various applications. The current survey aims review large literature last two decades regarding biosignal-based HMIs assistance rehabilitation outline state-of-the-art identify emerging technologies potential future research trends. PubMed other databases were surveyed by using specific keywords. found studies further screened in three levels (title, abstract, full-text), eventually, 144 journal papers 37 conference included. Four macrocategories considered classify different used HMI control: biopotential, muscle mechanical motion, body their combinations (hybrid systems). also classified according target application considering six categories: prosthetic control, robotic virtual reality gesture recognition, communication, smart environment control. An ever-growing number publications has been observed over years. Most (about 67%) pertain assistive field, while 20% relate 13% rehabilitation. A moderate increase can be focusing on recognition decade. In contrast, targets experienced only small increase. Biopotentials are no longer leading signals, use motion signals considerable rise, especially Hybrid promising, they could lead higher performances. However, HMIs' complexity, so usefulness should carefully evaluated application.

Language: Английский

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Developments in the human machine interface technologies and their applications: a review

Journal of Medical Engineering & Technology, Journal Year: 2021, Volume and Issue: 45(7), P. 552 - 573

Published: June 29, 2021

Human-machine interface (HMI) techniques use bioelectrical signals to gain real-time synchronised communication between the human body and machine functioning. HMI technology not only provides a control access but also has ability multiple functions at single instance of time with modest inputs increased efficiency. The technologies yield advanced on numerous applications such as health monitoring, medical diagnostics, development prosthetic assistive devices, automotive aerospace industry, robotic controls many more fields. In this paper, various physiological signals, their acquisition processing along respective in different have been discussed.

Language: Английский

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