المؤلفون: Guger, C., Allison, B., Kammerhofer, A., Guttmann, F., Von Oertzen, T. J., Spataro, R., La Bella, V., Annen, Jitka, Laureys, Steven, Heilinger, A., Ortner, R., Cho, W.

المصدر: 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, 3008-3013 (2017); 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, 5 October 2017 through 8 October 2017

مصطلحات موضوعية: Brain-computer interface, DOC, Locked-in syndrome, Motor imagery, P300, Bioelectric potentials, Cybernetics, Electric batteries, Eye movements, Interfaces (computer), Locks (fasteners), Amyotrophic lateral sclerosis, Classification accuracy, Clinical assessments, Cognitive capability, Communication reliabilities, Brain computer interface, Human health sciences, Neurology, Sciences de la santé humaine, Neurologie

الوصف: Patients with disorders of consciousness (DOC) cannot reply to questions or clinical assessments using voluntary motor control, and therefore it is very difficult to assess their cognitive capabilities and conscious awareness. Patients who are locked-in (LIS) are instead fully conscious, and they can communicate with their preserved eye movements. However, when the residual oculomotor activity is also lost (e.g., patients with amyotrophic lateral sclerosis disease of very long duration), the locked-in status becomes complete (CLIS). In CLIS patients, detection of conscious awareness may become very challenging, similarly to the subjects with DOC. mindBEAGLE has a physiological testing battery that uses auditory, vibro-tactile and motor imagery paradigms and braincomputer interface (BCI) technology to assess these patients and even provide communication for some of them. The current study presents results from 5 DOC and 3 LIS patients. The auditory evoked potential (AEP) assessement led to classification accuracies between 0 and 90 %, the vibro-tactile P300 paradigms led to 0 % to 100 % accuracy and the motor imagery paradigms led to accuracies up to 83.3 %. Three of the eight patients could succcessfully establish communication with the mindBEAGLE system. The results show that an assessment battery with auditory, vibrotactile and motor imagery paradigms is able to identify cognitive functions of DOC and LIS patients. Patients showed substantial fluctuations in EEG measures, assessment results and communication reliability across different days and runs. Therefore, it is important to have a system available that can quickly and easily determine the status of a patient. Successful communication with these patients is also important. © 2017 IEEE.

الوصول الحر: https://orbi.uliege.be/handle/2268/230699Test

المؤلفون: Arpaia P., D'Angelo M., D'Errico G., De Paolis L. T., Esposito A., Grassini S., Moccaldi N., Natalizio A., Nuzzo B. L.

المساهمون: Arpaia, P., D'Angelo, M., D'Errico, G., De Paolis, L. T., Esposito, A., Grassini, S., Moccaldi, N., Natalizio, A., Nuzzo, B. L.

مصطلحات موضوعية: BCI, Body Ownership, EEG, Extended Reality, Gamification, Motor Imagery, Neurofeedback, Virtual Reality

الوصف: The sense of body ownership, i.e., the experience of one's body as one's own, and the sense of agency, i.e., the feeling of control over bodily actions, are essential for bodily self-consciousness. Research on EEG-based brain-computer interface (BCI) has shown that individuals can retain a sense of agency and ownership even when they control virtual arms by imaging the movement but without physically performing it. Here, we investigated (i) if we are more accurate in controlling the movement of a virtual device qualified as part of one's own body and (ii) to what extent the EEG feature linked to the agency for one's own body parts and for external device differ. To this aim, participants use an EEGbased BCI to control two virtual arms presented either in a first-person perspective to induce both a sense of ownership and agency over the virtual arms, or in an anatomical incongruent position to retain only the sense of agency. Preliminary data (n=4) showed that there is no difference in the accuracy in controlling the virtual arms in the two conditions, as measured by the EEG decoding algorithm reflecting the motor intention of the user. Crucially, both conditions elicit a sense of agency over the virtual arms, although the sense of ownership was present only in the first-person perspective condition. If confirmed in the remaining participants to be tested (n=34), these results will suggest that the ability of controlling a virtual device is not affected by the sense of ownership felt over it. Therefore, motor control's accuracy and the subsequent sense of agency are the consequences of the association between an internal volitional signal and the external outcome, bypassing the actual body movements and the sense of body ownership. We provide a unique window into the relation between motor control and the sense of body ownership-findings that have important implications for daily life support of patients using neuroprosthetics.

وصف الملف: ELETTRONICO

العلاقة: info:eu-repo/semantics/altIdentifier/isbn/978-1-6654-8574-6; ispartofbook:2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings; 1st IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022; firstpage:104; lastpage:109; numberofpages:6; https://hdl.handle.net/11587/479605Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85144609925; https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9967507Test

الإتاحة: https://doi.org/10.1109/MetroXRAINE54828.2022.9967507Test
https://hdl.handle.net/11587/479605Test
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9967507Test

المؤلفون: Sagila Gangadharan, K., Benzy, V.K., Achutavarrier Prasad, Vinod

مصطلحات موضوعية: Direction decoding, Electroencephalogram, Motor Imagery, Time bin selection

الوصف: Motor-Imagery-based Brain Computer Interface (MI-BCI) decodes the parameters of imagined motor movement and translates it into control commands to the external world. It has potential applications in neurorehabilitation and development of assistive technology. This paper investigates the Electroencephalogram (EEG) correlates of direction parameters of a center-out hand movement imagination task in right and left directions. A variance-based time bin selection algorithm is proposed to select the most discriminative EEG time segment for directional classification of movement imagination. The discriminative EEG features carrying motor imagery (MI) directional information are extracted from the selected EEG time segment using the wavelet-common spatial pattern (WCSP) algorithm. The WCSP features are classified using Support Vector Machine classifier resulting in a cross validated classification accuracy of 71% between left versus right MI directions of 15 subjects. © 2021 IEEE.

العلاقة: http://repository.ust.hk/ir/Record/1783.1-116467Test; BioSMART 2021 - Proceedings: 4th International Conference on Bio-Engineering for Smart Technologies, December 2021, article number 9677887; https://doi.org/10.1109/BioSMART54244.2021.9677887Test; http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=&rft.volume=&rft.issue=&rft.date=2021&rft.spage=&rft.aulast=Sagila+Gangadharan&rft.aufirst=&rft.atitle=Maximum+Variance-based+EEG+Time+Bin+Selection+for+Decoding+of+Imagined+Hand+Movement+Directions+in+Brain+Computer+Interface&rft.title=BioSMART+2021+-+Proceedings%3A+4th+International+Conference+on+Bio-Engineering+for+Smart+TechnologiesTest; http://www.scopus.com/record/display.url?eid=2-s2.0-85125331669&origin=inwardTest

الإتاحة: https://doi.org/10.1109/BioSMART54244.2021.9677887Test
http://repository.ust.hk/ir/Record/1783.1-116467Test
http://lbdiscover.ust.hk/uresolver?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rfr_id=info:sid/HKUST:SPI&rft.genre=article&rft.issn=&rft.volume=&rft.issue=&rft.date=2021&rft.spage=&rft.aulast=Sagila+Gangadharan&rft.aufirst=&rft.atitle=Maximum+Variance-based+EEG+Time+Bin+Selection+for+Decoding+of+Imagined+Hand+Movement+Directions+in+Brain+Computer+Interface&rft.title=BioSMART+2021+-+Proceedings%3A+4th+International+Conference+on+Bio-Engineering+for+Smart+TechnologiesTest
http://www.scopus.com/record/display.url?eid=2-s2.0-85125331669&origin=inwardTest

المؤلفون: Wang X., Schneider T., Hersche M., Cavigelli L., Benini L.

المساهمون: Wang X., Schneider T., Hersche M., Cavigelli L., Benini L.

مصطلحات موضوعية: Brain-machine interface, Deep learning, Edge computing, Machine learning, Motor imagery, Parallel computing

الوصف: With Motor-Imagery (MI) Brain-Machine Interfaces (BMIs) we may control machines by merely thinking of performing a motor action. Practical use cases require a wearable solution where the classification of the brain signals is done locally near the sensor using machine learning models embedded on energy-efficient microcontroller units (MCUs), for assured privacy, user comfort, and long-term usage. In this work, we provide practical insights on the accuracy-cost tradeoff for embedded BMI solutions. Our proposed Multispectral Riemannian Classifier reaches 75.1% accuracy on 4-class MI task. We further scale down the model by quantizing it to mixed-precision representations with a minimal accuracy loss of 1%, which is still 3.2% more accurate than the state-of-the-art embedded convolutional neural network. We implement the model on a low-power MCU with parallel processing units taking only 33.39 ms and consuming 1.304 mJ per classification.

وصف الملف: ELETTRONICO

العلاقة: info:eu-repo/semantics/altIdentifier/isbn/978-1-7281-9201-7; info:eu-repo/semantics/altIdentifier/wos/WOS:000706507900087; ispartofbook:Proceedings - IEEE International Symposium on Circuits and Systems; 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021; volume:2021-; firstpage:1; lastpage:5; numberofpages:5; http://hdl.handle.net/11585/869392Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85109010829

الإتاحة: https://doi.org/10.1109/ISCAS51556.2021.9401564Test
http://hdl.handle.net/11585/869392Test

المؤلفون: Alberto Greco, Enzo Pasquale Scilingo, Stefano Di Modica, Laura Sebastiani, Antonio Lanata

مصطلحات موضوعية: Heartbeat, business.industry, Computer science, Feature extraction, Pattern recognition, Support vector machine, Motor imagery, Pattern recognition (psychology), Task analysis, Heart rate variability, Motor Imagery, Heart Rate, Variability, Machine Learning, Artificial intelligence, business, Set (psychology)

الوصف: This study investigates the assessment of motor imagery (MI) ability in humans through the analysis of heartbeat dynamics. Previous studies have demonstrated that MI processes strongly influence the autonomic nervous system (ANS) activity and, consequently, this reflects on the dynamics of ANS correlates such as the Heart Rate Variability (HRV). Here, we propose to extract a set of linear and nonlinear features from the HRV signals to characterize good and bad imagers. The feature set was used as input of a pattern recognition system based on the support vector machine in order to automatically recognize good and bad imagers using only cardiovascular information. To this aim, we designed an experiment where twenty volunteers performed visual and kinaesthetic imagery tasks. Results showed an accuracy of classification between good and bad imagers over 74%.

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f40517fcbf084219be8fdc5bc0034fbTest
http://hdl.handle.net/11568/1059811Test

المؤلفون: Susanna Mezzarobba, Pierpaolo Busan, Agostino Accardo, Piero Paolo Battaglini, Joanna Jarmolowska, Aleksandar Miladinović, Manuela Deodato, Giulia Silveri, Miloš Ajčević

المساهمون: Miladinovic, Aleksandar, Ajcevic, M., Busan, P., Jarmolowska, J., Silveri, G., Deodato, M., Mezzarobba, S., Battaglini, P. P., Accardo, A.

مصطلحات موضوعية: FOS: Computer and information sciences, medicine.medical_specialty, Brain-Computer Interface, Motor-Imagery, Parkinson's disease, Computer science, Common Spatial Filtering, Computer Science - Human-Computer Interaction, 02 engineering and technology, Electroencephalography, Human-Computer Interaction (cs.HC), 03 medical and health sciences, 0302 clinical medicine, Motor imagery, Physical medicine and rehabilitation, CSP, 0202 electrical engineering, electronic engineering, information engineering, medicine, Humans, BCI, neurorehabilitation, Neurorehabilitation, Brain–computer interface, medicine.diagnostic_test, Neurological Rehabilitation, 020206 networking & telecommunications, Parkinson Disease, medicine.disease, Brain-Computer Interfaces, Imagination, 030217 neurology & neurosurgery

الوصف: The study reports the performance of Parkinson's disease (PD) patients to operate Motor-Imagery based Brain-Computer Interface (MI-BCI) and compares three selected pre-processing and classification approaches. The experiment was conducted on 7 PD patients who performed a total of 14 MI-BCI sessions targeting lower extremities. EEG was recorded during the initial calibration phase of each session, and the specific BCI models were produced by using Spectrally weighted Common Spatial Patterns (SpecCSP), Source Power Comodulation (SPoC) and Filter-Bank Common Spatial Patterns (FBCSP) methods. The results showed that FBCSP outperformed SPoC in terms of accuracy, and both SPoC and SpecCSP in terms of the false-positive ratio. The study also demonstrates that PD patients were capable of operating MI-BCI, although with lower accuracy.

وصف الملف: ELETTRONICO

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::531fad531e647a622d426ba487b6dbe6Test
http://hdl.handle.net/11567/1070258Test

المؤلفون: Michael Hersche, Nobuaki Kobayashi, Luca Benini, Xiaying Wang, Thorir Mar Ingolfsson, Lukas Cavigelli

المساهمون: Ingolfsson T.M., Hersche M., Wang X., Kobayashi N., Cavigelli L., Benini L.

المصدر: SMC
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

مصطلحات موضوعية: Signal Processing (eess.SP), FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Brain–machine interface, Computer Science - Human-Computer Interaction, convolutional neural network, Machine Learning (stat.ML), 02 engineering and technology, Convolutional neural network, Human-Computer Interaction (cs.HC), Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, Motor imagery, edge computing, Statistics - Machine Learning, 0202 electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering, Electrical Engineering and Systems Science - Signal Processing, business.industry, Deep learning, deep learning, Motor-imagery, Convolutional Neural Networks (CNN), Edge Computing, Pattern recognition, motor-imagery, ComputingMethodologies_PATTERNRECOGNITION, Benchmark (computing), Memory footprint, 020201 artificial intelligence & image processing, Enhanced Data Rates for GSM Evolution, Artificial intelligence, business, brain-machine interface, 030217 neurology & neurosurgery

الوصف: In recent years, deep learning (DL) has contributed significantly to the improvement of motor-imagery brain–machine interfaces (MI-BMIs) based on electroencephalography (EEG). While achieving high classification accuracy, DL models have also grown in size, requiring a vast amount of memory and computational resources. This poses a major challenge to an embedded BMI solution that guarantees user privacy, reduced latency, and low power consumption by processing the data locally. In this paper, we propose EEG-TCNet, a novel temporal convolutional network (TCN) that achieves outstanding accuracy while requiring few trainable parameters. Its low memory footprint and low computational complexity for inference make it suitable for embedded classification on resource-limited devices at the edge. Experimental results on the BCI Competition IV-2a dataset show that EEG-TCNet achieves 77.35% classification accuracy in 4-class MI. By finding the optimal network hyperparameters per subject, we further improve the accuracy to 83.84%. Finally, we demonstrate the versatility of EEG-TCNet on the Mother of All BCI Benchmarks (MOABB), a large scale test benchmark containing 12 different EEG datasets with MI experiments. The results indicate that EEG-TCNet successfully generalizes beyond one single dataset, outperforming the current state-of-the-art (SoA) on MOABB by a meta-effect of 0.25.
2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
ISBN:978-1-7281-8527-9
ISBN:978-1-7281-8526-2

وصف الملف: ELETTRONICO; application/application/pdf

الوصول الحر: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::186b923abecdf6d8c95108b102af4724Test
https://hdl.handle.net/11585/800231Test

المؤلفون: L. Angrisani, P. Arpaia, F. Donnarumma, A. Esposito, Moccaldi, Nicola, M. Parvis

المساهمون: Angrisani, L., Arpaia, P., Donnarumma, F., Esposito, A., Moccaldi, Nicola, Parvis, M.

مصطلحات موضوعية: Brain-computer interfaces, Classification accuracy, Feature extraction, Motor imagery

الوصف: In this paper, the accuracy in classifying Motor Imagery (MI) tasks for a Brain-Computer Interface (BCI) is analyzed. Electroencephalographic (EEG) signals were taken into account, notably by employing one channel per time. Four classes were to distinguish, i.e. imagining the movement of left hand, right hand, feet, or tongue. The dataset '2a' of BCI Competition IV (2008) was considered. Brain signals were processed by applying a short-time Fourier transform, a common spatial pattern filter for feature extraction, and a support vector machine for classification. With this work, the aim is to give a contribution to the development of wearable MI-based BCIs by relying on single channel EEG.

العلاقة: info:eu-repo/semantics/altIdentifier/isbn/978-153863460-8; info:eu-repo/semantics/altIdentifier/wos/WOS:000568630900228; ispartofbook:Conference Record - IEEE Instrumentation and Measurement Technology Conference; 2019 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2019; volume:Volume 2019-May; numberofpages:5; http://hdl.handle.net/11588/767993Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85072846630

الإتاحة: https://doi.org/10.1109/I2MTC.2019.8827168Test
http://hdl.handle.net/11588/767993Test

المؤلفون: Das, Rig, Maiorana, Emanuele, Campisi, Patrizio

المساهمون: IEEE, Das, Rig, Maiorana, Emanuele, Campisi, Patrizio

مصطلحات موضوعية: Convolutional neural network, Electroencephalography, Motor imagery, Software, Signal Processing, Electrical and Electronic Engineering

الوصف: This paper deals with electroencephalography (EEG)-based biometric identification, using a motor imagery task, specifically performing imaginary arms and legs movements. Deep learning methods such as convolutional neural network (CNN) is used for automatic discriminative feature extraction and person identification. An extensive set of experimental tests, performed on a large database comprising EEG data collected from 40 subjects over two different sessions taken at a week distance, shows the existence of repeatable discriminative characteristics in individuals' brain signals.

العلاقة: info:eu-repo/semantics/altIdentifier/isbn/9781538646588; info:eu-repo/semantics/altIdentifier/wos/WOS:000446384602049; ispartofbook:ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018; volume:2018-; firstpage:2062; lastpage:2066; numberofpages:5; serie:PROCEEDINGS OF THE . IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING; http://hdl.handle.net/11590/347535Test; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85054219904

الإتاحة: https://doi.org/10.1109/ICASSP.2018.8461909Test
http://hdl.handle.net/11590/347535Test

المؤلفون: Choi, KS, Liang, S

مصطلحات موضوعية: Brain computer interface, Electroencephalography, Haptics, Motor imagery, P300, Steady state somatosensory evoked potential

الوصف: 19th IEEE International Symposium on Multimedia, ISM 2017, Taichung, Taiwan, 11-13 December 2017 ; Brain-computer interface (BCI) has been used as a communication tool to enable paralyzed people to interact with the world. Its application has been extended to other non-medical areas like self-regulation, marketing, games and entertainment. Conventionally, BCI largely relies on the visual perception channel to provide users with cues or stimuli for the generation of appropriate brain signals that can be identified accurately with classification algorithms. This could lead to visual fatigue and also distract the attention of users from the environment with which they are interacting. This paper explores the haptic perception channel for enhancing BCI performance. Analogous to the paradigms used in vision-based BCI, the corresponding P300 event related potential and steady state evoked potential in the haptics domain are discussed. Besides, the potential of using haptic feedback to improve and guide motor imagery in a way similar to that of visual feedback are also discussed. ; School of Nursing ; 2017-2018 > Academic research: refereed > Refereed conference paper ; 201811 bcma

العلاقة: IEEE International Symposium on Multimedia [ISM]; Proceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017, 2017, v. 2017-January, p. 450-452; http://hdl.handle.net/10397/79331Test; 450; 452; 2017-January; 2-s2.0-85045937393

الإتاحة: https://doi.org/10.1109/ISM.2017.89Test
http://hdl.handle.net/10397/79331Test