1,138 research outputs found

    Π˜Π½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΠ΅ крСсло-Ρ€ΠΎΠ±ΠΎΡ‚ со Π²ΡΠΏΠΎΠΌΠΎΠ³Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹ΠΌΠΈ срСдствами связи с использованиСм ΠΎΡ‚ΠΊΠ»ΠΈΠΊΠΎΠ² TEP ΠΈ характСристик Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π° спСктра Π±ΠΎΠ»Π΅Π΅ высокого порядка

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    In recent years, electroencephalography-based navigation and communication systems for differentially enabled communities have been progressively receiving more attention. To provide a navigation system with a communication aid, a customized protocol using thought evoked potentials has been proposed in this research work to aid the differentially enabled communities. This study presents the higher order spectra based features to categorize seven basic tasks that include Forward, Left, Right, Yes, NO, Help and Relax; that can be used for navigating a robot chair and also for communications using an oddball paradigm. The proposed system records the eight-channel wireless electroencephalography signal from ten subjects while the subject was perceiving seven different tasks. The recorded brain wave signals are pre-processed to remove the interference waveforms and segmented into six frequency band signals, i. e. Delta, Theta, Alpha, Beta, Gamma 1-1 and Gamma 2. The frequency band signals are segmented into frame samples of equal length and are used to extract the features using bispectrum estimation. Further, statistical features such as the average value of bispectral magnitude and entropy using the bispectrum field are extracted and formed as a feature set. The extracted feature sets are tenfold cross validated using multilayer neural network classifier. From the results, it is observed that the entropy of bispectral magnitude feature based classifier model has the maximum classification accuracy of 84.71 % and the value of the bispectral magnitude feature based classifier model has the minimum classification accuracy of 68.52 %.Π’ послСдниС Π³ΠΎΠ΄Ρ‹ всС большС внимания удСляСтся Π½Π°Π²ΠΈΠ³Π°Ρ†ΠΈΠΎΠ½Π½Ρ‹ΠΌ ΠΈ ΠΊΠΎΠΌΠΌΡƒΠ½ΠΈΠΊΠ°Ρ†ΠΈΠΎΠ½Π½Ρ‹ΠΌ систСмам Π½Π° основС элСктроэнцСфалограммы Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° для сообщСств с Ρ€Π°Π·Π½Ρ‹ΠΌΠΈ возмоТностями. Для прСдоставлСния Π½Π°Π²ΠΈΠ³Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠΉ систСмС Π²ΡΠΏΠΎΠΌΠΎΠ³Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… срСдств связи Π² Ρ€Π°Π±ΠΎΡ‚Π΅ ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½ настраиваСмый ΠΏΡ€ΠΎΡ‚ΠΎΠΊΠΎΠ», ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡŽΡ‰ΠΈΠΉ Π²Ρ‹Π·Π²Π°Π½Π½Ρ‹Π΅ ΠΌΡ‹ΡΠ»ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹Π΅ ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»Ρ‹, Ρ‡Ρ‚ΠΎΠ±Ρ‹ ΠΏΠΎΠΌΠΎΡ‡ΡŒ сообщСствам с Ρ€Π°Π·Π½Ρ‹ΠΌΠΈ возмоТностями. ΠŸΡ€Π΅Π΄ΡΡ‚Π°Π²Π»Π΅Π½Ρ‹ Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΈ, основанныС Π½Π° спСктрах Π±ΠΎΠ»Π΅Π΅ высокого порядка, для классификации сСми основных Π·Π°Π΄Π°Ρ‡, Ρ‚Π°ΠΊΠΈΡ… ΠΊΠ°ΠΊ Π’ΠΏΠ΅Ρ€Π΅Π΄, Π’Π»Π΅Π²ΠΎ, Π’ΠΏΡ€Π°Π²ΠΎ, Π”Π°, НЕВ, ΠŸΠΎΠΌΠΎΡ‰ΡŒ ΠΈ РасслаблСниС, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Ρ‚ΡŒ для управлСния крСслом-Ρ€ΠΎΠ±ΠΎΡ‚ΠΎΠΌ, Π° Ρ‚Π°ΠΊΠΆΠ΅ для связи с использованиСм Π½Π΅ΠΎΠ±Ρ‹Ρ‡Π½ΠΎΠΉ ΠΏΠ°Ρ€Π°Π΄ΠΈΠ³ΠΌΡ‹. ΠŸΡ€Π΅Π΄Π»Π°Π³Π°Π΅ΠΌΠ°Ρ систСма записываСт Π²ΠΎΡΡŒΠΌΠΈΠΊΠ°Π½Π°Π»ΡŒΠ½Ρ‹ΠΉ бСспроводной сигнал элСктроэнцСфалографии ΠΎΡ‚ дСсяти ΡΡƒΠ±ΡŠΠ΅ΠΊΡ‚ΠΎΠ², Π² Ρ‚ΠΎ врСмя ΠΊΠ°ΠΊ ΡΡƒΠ±ΡŠΠ΅ΠΊΡ‚ воспринимал сСмь Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… Π·Π°Π΄Π°Ρ‡. ЗаписанныС сигналы ΠΌΠΎΠ·Π³ΠΎΠ²Ρ‹Ρ… Π²ΠΎΠ»Π½ ΠΏΡ€Π΅Π΄Π²Π°Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ ΠΎΠ±Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‚ΡΡ для удалСния ΠΈΠ½Ρ‚Π΅Ρ€Ρ„Π΅Ρ€Π΅Π½Ρ†ΠΈΠΎΠ½Π½Ρ‹Ρ… Π²ΠΎΠ»Π½ ΠΈ ΡΠ΅Π³ΠΌΠ΅Π½Ρ‚ΠΈΡ€ΡƒΡŽΡ‚ΡΡ Π½Π° сигналы ΡˆΠ΅ΡΡ‚ΠΈ частотных Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½ΠΎΠ²: Π΄Π΅Π»ΡŒΡ‚Π°, Ρ‚Π΅Ρ‚Π°, Π°Π»ΡŒΡ„Π°, Π±Π΅Ρ‚Π°, Π³Π°ΠΌΠΌΠ° 1-1 ΠΈ Π³Π°ΠΌΠΌΠ° 2. Π‘ΠΈΠ³Π½Π°Π»Ρ‹ полосы частот ΡΠ΅Π³ΠΌΠ΅Π½Ρ‚ΠΈΡ€ΡƒΡŽΡ‚ΡΡ Π½Π° Π²Ρ‹Π±ΠΎΡ€ΠΊΠΈ ΠΊΠ°Π΄Ρ€ΠΎΠ² Ρ€Π°Π²Π½ΠΎΠΉ Π΄Π»ΠΈΠ½Ρ‹ ΠΈ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΡƒΡŽΡ‚ΡΡ для извлСчСния ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ² с использованиСм ΠΎΡ†Π΅Π½ΠΊΠΈ биспСктра. ΠšΡ€ΠΎΠΌΠ΅ Ρ‚ΠΎΠ³ΠΎ, статистичСскиС характСристики, Ρ‚Π°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ срСднСС Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ Π±ΠΈΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½ΠΎΠΉ Π²Π΅Π»ΠΈΡ‡ΠΈΠ½Ρ‹ ΠΈ энтропия с использованиСм области биспСктра, ΠΈΠ·Π²Π»Π΅ΠΊΠ°ΡŽΡ‚ΡΡ ΠΈ Ρ„ΠΎΡ€ΠΌΠΈΡ€ΡƒΡŽΡ‚ΡΡ ΠΊΠ°ΠΊ Π½Π°Π±ΠΎΡ€ характСристик. Π˜Π·Π²Π»Π΅Ρ‡Π΅Π½Π½Ρ‹Π΅ Π½Π°Π±ΠΎΡ€Ρ‹ Ρ„ΡƒΠ½ΠΊΡ†ΠΈΠΉ проходят Π΄Π΅ΡΡΡ‚ΠΈΠΊΡ€Π°Ρ‚Π½ΡƒΡŽ ΠΏΠ΅Ρ€Π΅ΠΊΡ€Π΅ΡΡ‚Π½ΡƒΡŽ ΠΏΡ€ΠΎΠ²Π΅Ρ€ΠΊΡƒ с использованиСм классификатора многослойной Π½Π΅ΠΉΡ€ΠΎΠ½Π½ΠΎΠΉ сСти. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, Ρ‡Ρ‚ΠΎ энтропия ΠΌΠΎΠ΄Π΅Π»ΠΈ классификатора Π½Π° основС характСристик Π±ΠΈΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½ΠΎΠΉ Π²Π΅Π»ΠΈΡ‡ΠΈΠ½Ρ‹ ΠΈΠΌΠ΅Π΅Ρ‚ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡŒΠ½ΡƒΡŽ Ρ‚ΠΎΡ‡Π½ΠΎΡΡ‚ΡŒ классификации 84,71 %, Π° срСднСС Π·Π½Π°Ρ‡Π΅Π½ΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ классификатора Π½Π° основС характСристик Π±ΠΈΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½ΠΎΠΉ Π²Π΅Π»ΠΈΡ‡ΠΈΠ½Ρ‹ – ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΡŒΠ½ΡƒΡŽ Ρ‚ΠΎΡ‡Π½ΠΎΡΡ‚ΡŒ классификации 68,52 %

    Intelligent sensing technologies for the diagnosis, monitoring and therapy of alzheimer’s disease:A systematic review

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    Alzheimer’s disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer’s patients with, continuous monitoring, functional support and timely therapeutic interventions for whom these are of paramount importance. This review aims to present a summary of such systems reported in the extant literature for the management of Alzheimer’s disease. Four databases were searched, and 253 English language articles were identified published between the years 2015 to 2020. Through a series of filtering mechanisms, 20 articles were found suitable to be included in this review. This study gives an overview of the depth and breadth of the efficacy as well as the limitations of these intelligent systems proposed for Alzheimer’s. Results indicate two broad categories of intelligent technologies, distributed systems and self-contained devices. Distributed systems base their outcomes mostly on long-term monitoring activity patterns of individuals whereas handheld devices give quick assessments through touch, vision and voice. The review concludes by discussing the potential of these intelligent technologies for clinical practice while highlighting future considerations for improvements in the design of these solutions for Alzheimer’s disease

    Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm

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    [EN] Robotics has been successfully applied in the design of collaborative robots for assistance to people with motor disabilities. However, man-machine interaction is difficult for those who suffer severe motor disabilities. The aim of this study was to test the feasibility of a low-cost robotic arm control system with an EEG-based brain-computer interface (BCI). The BCI system relays on the Steady State Visually Evoked Potentials (SSVEP) paradigm. A cross-platform application was obtained in C++. This C++ platform, together with the open-source software Openvibe was used to control a Staubli robot arm model TX60. Communication between Openvibe and the robot was carried out through the Virtual Reality Peripheral Network (VRPN) protocol. EEG signals were acquired with the 8-channel Enobio amplifier from Neuroelectrics. For the processing of the EEG signals, Common Spatial Pattern (CSP) filters and a Linear Discriminant Analysis classifier (LDA) were used. Five healthy subjects tried the BCI. This work allowed the communication and integration of a well-known BCI development platform such as Openvibe with the specific control software of a robot arm such as Staubli TX60 using the VRPN protocol. It can be concluded from this study that it is possible to control the robotic arm with an SSVEP-based BCI with a reduced number of dry electrodes to facilitate the use of the system.Funding for open access charge: Universitat Politecnica de Valencia.Quiles Cucarella, E.; Dadone, J.; Chio, N.; GarcΓ­a Moreno, E. (2022). Cross-Platform Implementation of an SSVEP-Based BCI for the Control of a 6-DOF Robotic Arm. Sensors. 22(13):1-26. https://doi.org/10.3390/s22135000126221

    On Tackling Fundamental Constraints in Brain-Computer Interface Decoding via Deep Neural Networks

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    A Brain-Computer Interface (BCI) is a system that provides a communication and control medium between human cortical signals and external devices, with the primary aim to assist or to be used by patients who suffer from a neuromuscular disease. Despite significant recent progress in the area of BCI, there are numerous shortcomings associated with decoding Electroencephalography-based BCI signals in real-world environments. These include, but are not limited to, the cumbersome nature of the equipment, complications in collecting large quantities of real-world data, the rigid experimentation protocol and the challenges of accurate signal decoding, especially in making a system work in real-time. Hence, the core purpose of this work is to investigate improving the applicability and usability of BCI systems, whilst preserving signal decoding accuracy. Recent advances in Deep Neural Networks (DNN) provide the possibility for signal processing to automatically learn the best representation of a signal, contributing to improved performance even with a noisy input signal. Subsequently, this thesis focuses on the use of novel DNN-based approaches for tackling some of the key underlying constraints within the area of BCI. For example, recent technological improvements in acquisition hardware have made it possible to eliminate the pre-existing rigid experimentation procedure, albeit resulting in noisier signal capture. However, through the use of a DNN-based model, it is possible to preserve the accuracy of the predictions from the decoded signals. Moreover, this research demonstrates that by leveraging DNN-based image and signal understanding, it is feasible to facilitate real-time BCI applications in a natural environment. Additionally, the capability of DNN to generate realistic synthetic data is shown to be a potential solution in reducing the requirement for costly data collection. Work is also performed in addressing the well-known issues regarding subject bias in BCI models by generating data with reduced subject-specific features. The overall contribution of this thesis is to address the key fundamental limitations of BCI systems. This includes the unyielding traditional experimentation procedure, the mandatory extended calibration stage and sustaining accurate signal decoding in real-time. These limitations lead to a fragile BCI system that is demanding to use and only suited for deployment in a controlled laboratory. Overall contributions of this research aim to improve the robustness of BCI systems and enable new applications for use in the real-world

    Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

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    We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications

    Recent Developments in Smart Healthcare

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    Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine
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