566 research outputs found
ΠΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ»Π΅ΠΊΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΌΠΎΠ·Π³Π°, ΡΠ²ΡΠ·Π°Π½Π½ΠΎΠΉ Ρ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡΠΌΠΈ: ΠΎΠ±Π·ΠΎΡ
Π ΠΎΠ±ΠΎΡΠ° ΠΏΡΠΈΡΠ²ΡΡΠ΅Π½Π° ΡΠΎΠ·Π³Π»ΡΠ΄Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌ, ΡΠΎ Π²ΠΈΠ½ΠΈΠΊΠ°ΡΡΡ ΠΏΡΠΈ Π΄ΠΎΡΠ»ΡΠ΄ΠΆΠ΅Π½Π½Ρ Π΄ΡΡΠ»ΡΠ½ΠΎΡΡΡ ΠΌΠΎΠ·ΠΊΡ, ΠΏΠΎΠ²'ΡΠ·Π°Π½ΠΎΡ Π· ΡΡΡ
Π°ΠΌΠΈ. ΠΠΌΡΠ½ΠΈ Π² ΠΊΠΎΡΡ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·ΠΊΡ ΠΏΡΠ΄ ΡΠ°Ρ Π²ΠΈΠΊΠΎΠ½Π°Π½Π½Ρ ΡΡΡ
Ρ, Π° ΡΠ°ΠΊΠΎΠΆ ΠΉΠΎΠ³ΠΎ ΡΡΠ²Π»Π΅Π½Π½Ρ, Π²ΡΠ΄ΠΎΠ±ΡΠ°ΠΆΠ°ΡΡΡ Π½Π΅ΠΉΡΠΎΠ½Π½Ρ ΠΌΠ΅ΡΠ΅ΠΆΡ, ΡΡΠΎΡΠΌΠΎΠ²Π°Π½Ρ Π΄Π»Ρ ΠΏΠ»Π°Π½ΡΠ²Π°Π½Π½Ρ Ρ ΡΠ΅Π°Π»ΡΠ·Π°ΡΡΡ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΡΡ
Ρ. ΠΠ°Π²Π΅Π΄Π΅Π½ΠΎ ΠΎΠ³Π»ΡΠ΄ ΠΌΠ΅ΡΠΎΠ΄ΡΠ² ΠΏΠ΅ΡΠ²ΠΈΠ½Π½ΠΎΡ ΠΎΠ±ΡΠΎΠ±ΠΊΠΈ Π·Π°ΡΠ΅ΡΡΡΡΠΎΠ²Π°Π½ΠΎΡ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·ΠΊΡ, ΡΠΊΡ ΠΌΠΎΠΆΡΡΡ Π±ΡΡΠΈ Π²ΠΈΠΊΠΎΡΠΈΡΡΠ°Π½Ρ Π΄Π»Ρ ΠΏΡΠ΄Π²ΠΈΡΠ΅Π½Π½Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ Π²ΠΈΠ΄ΡΠ»Π΅Π½ΠΈΡ
ΠΎΠ·Π½Π°ΠΊ. ΠΠΏΠΈΡΠ°Π½ΠΎ Π·Π°ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½ΠΎΡΡΡ, ΡΠΊΡ ΠΌΠ°ΡΡΡ ΠΌΡΡΡΠ΅ Π΄ΠΎ ΠΏΠΎΡΠ°ΡΠΊΡ ΡΡΡ
Ρ Ρ ΠΏΡΡΠ»Ρ Π½ΡΠΎΠ³ΠΎ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄ΠΈ, ΡΠΊΡ ΠΏΡΠ΄Ρ
ΠΎΠ΄ΡΡΡ Π΄Π»Ρ ΠΎΡΡΠ½ΠΊΠΈ Π·Π²'ΡΠ·ΠΊΡ ΡΠΊ ΠΌΡΠΆ Π°ΠΊΡΠΈΠ²Π½ΡΡΡΡ ΠΌΠΎΠ·ΠΊΡ Ρ Π°ΠΊΡΠΈΠ²Π½ΡΡΡΡ ΠΌ'ΡΠ·ΡΠ², ΡΠ°ΠΊ Ρ ΠΌΡΠΆ Π°ΠΊΡΠΈΠ²Π½ΡΡΡΡ ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·ΠΊΡ. ΠΡΡΠΌ ΡΠΎΠ³ΠΎ, ΡΠΎΠ·Π³Π»ΡΠ½ΡΡΠ° ΠΌΠΎΠΆΠ»ΠΈΠ²ΡΡΡΡ ΠΊΠ»Π°ΡΠΈΡΡΠΊΠ°ΡΡΡ ΡΠ° ΠΏΡΠΎΠ³Π½ΠΎΠ·ΡΠ²Π°Π½Π½Ρ ΡΡΡ
ΡΠ² ΡΠ°Π·ΠΎΠΌ Π· ΡΠ΅ΠΊΠΎΠ½ΡΡΡΡΠΊΡΡΡΡ ΠΊΡΠ½Π΅ΠΌΠ°ΡΠΈΡΠ½ΠΈΡ
Π²Π»Π°ΡΡΠΈΠ²ΠΎΡΡΠ΅ΠΉ.The work is devoted to consideration of different problems which arise in studying of the movement-related brain activity. Changes in the cortex activity during performing of the movement both real and imagery represent neural networks formed for planning and performing of the particular motion. The review of possible preprocessing methods of the registered brain activity for increasing significance of extracted features are shown. Regularities and patterns which take place before and after movement onset are described. The methods that suitable for connectivity estimations in case of cortico-muscular relationships and in case of evaluations between brain regions are shown. In addition, possibility of movement classification and prediction together with reconstruction of kinematics features of the motion are considered.Π Π°Π±ΠΎΡΠ° ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½ΠΈΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌ, Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΠΈΡ
ΠΏΡΠΈ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠΈ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΌΠΎΠ·Π³Π°, ΡΠ²ΡΠ·Π°Π½Π½ΠΎΠΉ Ρ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡΠΌΠΈ. ΠΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ Π² ΠΊΠΎΡΠ΅ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° Π²ΠΎ Π²ΡΠ΅ΠΌΡ Π²ΡΠΏΠΎΠ»Π½Π΅Π½ΠΈΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ, Π° ΡΠ°ΠΊΠΆΠ΅ Π΅Π³ΠΎ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΈΡ, ΠΎΡΠΎΠ±ΡΠ°ΠΆΠ°ΡΡ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠ΅ ΡΠ΅ΡΠΈ, ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ Π΄Π»Ρ ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ ΡΠ΅Π°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΎΠ³ΠΎ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ. ΠΡΠΈΠ²Π΅Π΄Π΅Π½ ΠΎΠ±Π·ΠΎΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΏΠ΅ΡΠ²ΠΈΡΠ½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π·Π°ΡΠ΅Π³ΠΈΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π°, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Ρ Π΄Π»Ρ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΠΈ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ². ΠΠΏΠΈΡΠ°Π½Ρ Π·Π°ΠΊΠΎΠ½ΠΎΠΌΠ΅ΡΠ½ΠΎΡΡΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΈΠΌΠ΅ΡΡ ΠΌΠ΅ΡΡΠΎ Π΄ΠΎ Π½Π°ΡΠ°Π»Π° Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΡ ΠΈ ΠΏΠΎΡΠ»Π΅ Π½Π΅Π³ΠΎ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΌΠ΅ΡΠΎΠ΄Ρ, ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΡΡΠΈΠ΅ Π΄Π»Ρ ΠΎΡΠ΅Π½ΠΊΠΈ ΡΠ²ΡΠ·ΠΈ ΠΊΠ°ΠΊ ΠΌΠ΅ΠΆΠ΄Ρ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡΡ ΠΌΠΎΠ·Π³Π° ΠΈ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡΡ ΠΌΡΡΡ, ΡΠ°ΠΊ ΠΈ ΠΌΠ΅ΠΆΠ΄Ρ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΡΡ ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π°. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Π° Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΉ Π²ΠΌΠ΅ΡΡΠ΅ Ρ ΡΠ΅ΠΊΠΎΠ½ΡΡΡΡΠΊΡΠΈΠ΅ΠΉ ΠΊΠΈΠ½Π΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ²ΠΎΠΉΡΡΠ²
Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation
Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations
Analysis of Human Gait Using Hybrid EEG-fNIRS-Based BCI System: A Review
Human gait is a complex activity that requires high coordination between the central nervous system, the limb, and the musculoskeletal system. More research is needed to understand the latter coordination\u27s complexity in designing better and more effective rehabilitation strategies for gait disorders. Electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) are among the most used technologies for monitoring brain activities due to portability, non-invasiveness, and relatively low cost compared to others. Fusing EEG and fNIRS is a well-known and established methodology proven to enhance brainβcomputer interface (BCI) performance in terms of classification accuracy, number of control commands, and response time. Although there has been significant research exploring hybrid BCI (hBCI) involving both EEG and fNIRS for different types of tasks and human activities, human gait remains still underinvestigated. In this article, we aim to shed light on the recent development in the analysis of human gait using a hybrid EEG-fNIRS-based BCI system. The current review has followed guidelines of preferred reporting items for systematic reviews and meta-Analyses (PRISMA) during the data collection and selection phase. In this review, we put a particular focus on the commonly used signal processing and machine learning algorithms, as well as survey the potential applications of gait analysis. We distill some of the critical findings of this survey as follows. First, hardware specifications and experimental paradigms should be carefully considered because of their direct impact on the quality of gait assessment. Second, since both modalities, EEG and fNIRS, are sensitive to motion artifacts, instrumental, and physiological noises, there is a quest for more robust and sophisticated signal processing algorithms. Third, hybrid temporal and spatial features, obtained by virtue of fusing EEG and fNIRS and associated with cortical activation, can help better identify the correlation between brain activation and gait. In conclusion, hBCI (EEG + fNIRS) system is not yet much explored for the lower limb due to its complexity compared to the higher limb. Existing BCI systems for gait monitoring tend to only focus on one modality. We foresee a vast potential in adopting hBCI in gait analysis. Imminent technical breakthroughs are expected using hybrid EEG-fNIRS-based BCI for gait to control assistive devices and Monitor neuro-plasticity in neuro-rehabilitation. However, although those hybrid systems perform well in a controlled experimental environment when it comes to adopting them as a certified medical device in real-life clinical applications, there is still a long way to go
Decoding Movement Direction for Brain-Computer Interfaces using Depth and Surface EEG Recordings
A Brain-Computer Interface (BCI) is a communication system between the brain and an external device. The purpose of this project was to develop a decoder for a BCI system capable of providing a control output based on decoding of the intention of movement and of different directions of movement execution, which in turn will enhance the quality of the command to external systems to propitiate the restoration of more complex motor functions than the two-choice commands commonly available in literature. The system was based on classification of invasive and non-invasive brain signal recordings. Results of the detection of the intention of movement and the classification of the direction were significantly above the level of chance for both iEEG and scalp EEG data. The present study also enabled to set a comparison of different methods used for spatial filtering, normalization and classificatio
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brainβmachine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
Advances in Neural Signal Processing
Neural signal processing is a specialized area of signal processing aimed at extracting information or decoding intent from neural signals recorded from the central or peripheral nervous system. This has significant applications in the areas of neuroscience and neural engineering. These applications are famously known in the area of brainβmachine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications
Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review
Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed
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