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    ИсслСдованиС элСктричСской активности ΠΌΠΎΠ·Π³Π°, связанной с двиТСниями: ΠΎΠ±Π·ΠΎΡ€

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    Π ΠΎΠ±ΠΎΡ‚Π° присвячСна розгляду ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌ, Ρ‰ΠΎ Π²ΠΈΠ½ΠΈΠΊΠ°ΡŽΡ‚ΡŒ ΠΏΡ€ΠΈ дослідТСнні Π΄Ρ–ΡΠ»ΡŒΠ½ΠΎΡΡ‚Ρ– ΠΌΠΎΠ·ΠΊΡƒ, ΠΏΠΎΠ²'язаної Π· Ρ€ΡƒΡ…Π°ΠΌΠΈ. Π—ΠΌΡ–Π½ΠΈ Π² ΠΊΠΎΡ€Ρ– Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·ΠΊΡƒ ΠΏΡ–Π΄ час виконання Ρ€ΡƒΡ…Ρƒ, Π° Ρ‚Π°ΠΊΠΎΠΆ ΠΉΠΎΠ³ΠΎ уявлСння, Π²Ρ–Π΄ΠΎΠ±Ρ€Π°ΠΆΠ°ΡŽΡ‚ΡŒ Π½Π΅ΠΉΡ€ΠΎΠ½Π½Ρ– ΠΌΠ΅Ρ€Π΅ΠΆΡ–, сформовані для планування Ρ– Ρ€Π΅Π°Π»Ρ–Π·Π°Ρ†Ρ–Ρ— ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠ³ΠΎ Ρ€ΡƒΡ…Ρƒ. НавСдСно огляд ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ–Π² ΠΏΠ΅Ρ€Π²ΠΈΠ½Π½ΠΎΡ— ΠΎΠ±Ρ€ΠΎΠ±ΠΊΠΈ зарСєстрованої активності Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·ΠΊΡƒ, які ΠΌΠΎΠΆΡƒΡ‚ΡŒ Π±ΡƒΡ‚ΠΈ використані для підвищСння значимості Π²ΠΈΠ΄Ρ–Π»Π΅Π½ΠΈΡ… ΠΎΠ·Π½Π°ΠΊ. Описано закономірності, які ΠΌΠ°ΡŽΡ‚ΡŒ місцС Π΄ΠΎ ΠΏΠΎΡ‡Π°Ρ‚ΠΊΡƒ Ρ€ΡƒΡ…Ρƒ Ρ– після нього. ΠŸΡ€Π΅Π΄ΡΡ‚Π°Π²Π»Π΅Π½Ρ– ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈ, які ΠΏΡ–Π΄Ρ…ΠΎΠ΄ΡΡ‚ΡŒ для ΠΎΡ†Ρ–Π½ΠΊΠΈ Π·Π²'язку як ΠΌΡ–ΠΆ Π°ΠΊΡ‚ΠΈΠ²Π½Ρ–ΡΡ‚ΡŽ ΠΌΠΎΠ·ΠΊΡƒ Ρ– Π°ΠΊΡ‚ΠΈΠ²Π½Ρ–ΡΡ‚ΡŽ ΠΌ'язів, Ρ‚Π°ΠΊ Ρ– ΠΌΡ–ΠΆ Π°ΠΊΡ‚ΠΈΠ²Π½Ρ–ΡΡ‚ΡŽ областСй Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·ΠΊΡƒ. ΠšΡ€Ρ–ΠΌ Ρ‚ΠΎΠ³ΠΎ, розглянута ΠΌΠΎΠΆΠ»ΠΈΠ²Ρ–ΡΡ‚ΡŒ класифікації Ρ‚Π° прогнозування Ρ€ΡƒΡ…Ρ–Π² Ρ€Π°Π·ΠΎΠΌ Π· Ρ€Π΅ΠΊΠΎΠ½ΡΡ‚Ρ€ΡƒΠΊΡ†Ρ–Ρ”ΡŽ ΠΊΡ–Π½Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π½ΠΈΡ… властивостСй.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±ΠΎΡ‚Π° посвящСна Ρ€Π°ΡΡΠΌΠΎΡ‚Ρ€Π΅Π½ΠΈΡŽ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌ, Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡŽΡ‰ΠΈΡ… ΠΏΡ€ΠΈ ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠΈ Π΄Π΅ΡΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ ΠΌΠΎΠ·Π³Π°, связанной с двиТСниями. ИзмСнСния Π² ΠΊΠΎΡ€Π΅ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° Π²ΠΎ врСмя выполнСния двиТСния, Π° Ρ‚Π°ΠΊΠΆΠ΅ Π΅Π³ΠΎ прСдставлСния, ΠΎΡ‚ΠΎΠ±Ρ€Π°ΠΆΠ°ΡŽΡ‚ Π½Π΅ΠΉΡ€ΠΎΠ½Π½Ρ‹Π΅ сСти, сформированныС для планирования ΠΈ Ρ€Π΅Π°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ ΠΊΠΎΠ½ΠΊΡ€Π΅Ρ‚Π½ΠΎΠ³ΠΎ двиТСния. ΠŸΡ€ΠΈΠ²Π΅Π΄Π΅Π½ ΠΎΠ±Π·ΠΎΡ€ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² ΠΏΠ΅Ρ€Π²ΠΈΡ‡Π½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ зарСгистрированной активности Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π°, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ для ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΡ значимости Π²Ρ‹Π΄Π΅Π»Π΅Π½Π½Ρ‹Ρ… ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ². ΠžΠΏΠΈΡΠ°Π½Ρ‹ закономСрности, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ ΠΈΠΌΠ΅ΡŽΡ‚ мСсто Π΄ΠΎ Π½Π°Ρ‡Π°Π»Π° двиТСния ΠΈ послС Π½Π΅Π³ΠΎ. ΠŸΡ€Π΅Π΄ΡΡ‚Π°Π²Π»Π΅Π½Ρ‹ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹, подходящиС для ΠΎΡ†Π΅Π½ΠΊΠΈ связи ΠΊΠ°ΠΊ ΠΌΠ΅ΠΆΠ΄Ρƒ Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒΡŽ ΠΌΠΎΠ·Π³Π° ΠΈ Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒΡŽ ΠΌΡ‹ΡˆΡ†, Ρ‚Π°ΠΊ ΠΈ ΠΌΠ΅ΠΆΠ΄Ρƒ Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒΡŽ областСй Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π°. ΠšΡ€ΠΎΠΌΠ΅ Ρ‚ΠΎΠ³ΠΎ, рассмотрСна Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡ‚ΡŒ классификации ΠΈ прогнозирования Π΄Π²ΠΈΠΆΠ΅Π½ΠΈΠΉ вмСстС с рСконструкциСй кинСматичСских свойств

    Real-time EMG based pattern recognition control for hand prostheses : a review on existing methods, challenges and future implementation

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    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

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    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 brain–computer 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

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    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

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    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 brain–machine interfaces. This book presents recent advances in this flourishing field of neural signal processing with demonstrative applications

    Advances in Neural Signal Processing

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    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 brain–machine 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

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    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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