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Computer brain interface

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https://www.readbyqxmd.com/read/28730995/decoding-human-mental-states-by-whole-head-eeg-fnirs-during-category-fluency-task-performance
#1
Ahmet Omurtag, Haleh Aghajani, Hasan Onur Keles
Concurrent scalp electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), which we refer to as EEG+fNIRS, promises greater accuracy than the individual modalities while remaining nearly as convenient as EEG. We sought to quantify the hybrid system's ability to decode mental states and compare it with its unimodal components. Approach. We recorded from healthy volunteers taking the category fluency test and applied machine learning techniques to the data. Main results...
July 21, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28729830/resting-state-fluctuations-of-eeg-sensorimotor-rhythm-reflect-bold-activities-in-the-pericentral-areas-a-simultaneous-eeg-fmri-study
#2
Shohei Tsuchimoto, Shuka Shibusawa, Nobuaki Mizuguchi, Kenji Kato, Hiroki Ebata, Meigen Liu, Takashi Hanakawa, Junichi Ushiba
Blockade of the scalp electroencephalographic (EEG) sensorimotor rhythm (SMR) is a well-known phenomenon following attempted or executed motor functions. Such a frequency-specific power attenuation of the SMR occurs in the alpha and beta frequency bands and is spatially registered at primary somatosensory and motor cortices. Here, we hypothesized that resting-state fluctuations of the SMR in the alpha and beta frequency bands also covary with resting-state sensorimotor cortical activity, without involving task-related neural dynamics...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28729822/low-intensity-focused-tdcs-over-the-motor-cortex-shows-inefficacy-to-improve-motor-imagery-performance
#3
Irma N Angulo-Sherman, Marisol Rodríguez-Ugarte, Eduardo Iáñez, Jose M Azorín
Transcranial direct current stimulation (tDCS) is a brain stimulation technique that can enhance motor activity by stimulating the motor path. Thus, tDCS has the potential of improving the performance of brain-computer interfaces during motor neurorehabilitation. tDCS effects depend on several aspects, including the current density, which usually varies between 0.02 and 0.08 mA/cm(2), and the location of the stimulation electrodes. Hence, testing tDCS montages at several current levels would allow the selection of current parameters for improving stimulation outcomes and the comparison of montages...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28727555/tid-introducing-and-benchmarking-an-event-delivery-system-for-brain-computer-interfaces
#4
Christian Breitwieser, Michele Tavella, Martijn Schreuder, Febo Cincotti, Robert Leeb, Gernot R Muller-Putz
In this paper, we present and analyze an event distribution system for brain-computer interfaces (BCIs). Events are commonly used to mark and describe incidents during an experiment and are therefore critical for later data analysis or immediate real-time processing. The presented approach, called TiD (Tools for brain-computer interaction - interface D), delivers messages in XML format via a bus-like system using TCP (transmission control protocol) connections or shared memory. A dedicated server dispatches TiD messages to distributed or local clients...
July 18, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28727554/a-human-humanoid-interaction-through-the-use-of-bci-for-locked-in-als-patients-using-neuro-biological-feedback-fusion
#5
Rosario Sorbello, Salvatore Tramonte, Marcello Giardina, Vincenzo La Bella, Rossella Spataro, Brendan Allison, Christoph Guger, Antonio Chella
This paper illustrates a new architecture for a human-humanoid interaction based on EEG-Brain Computer Interface (EEG-BCI) for patients affected by locked-in syndrome caused by Amyotrophic Lateral Sclerosis (ALS). The proposed architecture is able to recognise users' mental state accordingly to the biofeedback factor Bf , based on users' Attention, Intention and Focus, that is used to elicit a robot to perform customised behaviours. Experiments have been conducted with a population of 8 subjects: 4 ALS patients in a near Locked-in status with normal ocular movement and 4 healthy control subjects enrolled for age, education and computer expertise...
July 18, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28722685/neural-control-of-finger-movement-via-intracortical-brain-machine-interface
#6
Zachary T Irwin, Karen E Schroeder, Philip P Vu, Autumn J Bullard, Derek M Tat, Chrono S Nu, Alex Vaskov, Samuel R Nason, David E Thompson, Nicole Bentley, Parag G Patil, Cynthia A Chestek
OBJECTIVE: Intracortical brain-machine interfaces (BMIs) are a promising source of prosthesis control signals for individuals with severe motor disabilities. Previous BMI studies have primarily focused on predicting and controlling whole-arm movements; precise control of hand kinematics, however, has not been fully demonstrated. Here, we investigate the continuous decoding of precise finger movements in rhesus macaques. APPROACH: In order to elicit precise and repeatable finger movements, we have developed a novel behavioral task paradigm which requires the subject to acquire virtual fingertip position targets...
July 19, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28718781/latent-variable-method-for-automatic-adaptation-to-background-states-in-motor-imagery-bci
#7
Nikolay Dagaev, Ksenia Volkova, Alexei Ossadtchi
<i>Objective</i>. Brain-computer interface (BCI) systems are known to be vulnerable to variabilities in background states of a user. Usually, no detailed information on these states is available even during the training stage. Thus there is a need in a method which is capable of taking background states into account in an unsupervised way. <i>Approach</i>. We propose a latent variable method that is based on a probabilistic model with a discrete latent variable. In order to estimate the model's parameters, we suggest to use the expectation maximization (EM) algorithm...
July 18, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28715332/current-source-density-estimation-enhances-the-performance-of-motor-imagery-related-brain-computer-interface
#8
Dheeraj Rathee, Haider Raza, Girijesh Prasad, Hubert Cecotti
The objective is to evaluate the impact of EEG referencing schemes and spherical surface Laplacian (SSL) methods on the classification performance of motor-imagery (MI) related brain-computer interface systems. Two EEG referencing schemes: common referencing, common average referencing (CAR) and three surface Laplacian methods: current source density (CSD), finite difference method, and SSL using realistic head model, were implemented separately for pre-processing of the EEG signals recorded at the scalp. A combination of filter bank common spatial filter for features extraction and support vector machine for classification was used for both pairwise binary classifications and four-class classification of MI tasks...
July 13, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28713235/connecting-the-brain-to-itself-through-an-emulation
#9
Mijail D Serruya
Pilot clinical trials of human patients implanted with devices that can chronically record and stimulate ensembles of hundreds to thousands of individual neurons offer the possibility of expanding the substrate of cognition. Parallel trains of firing rate activity can be delivered in real-time to an array of intermediate external modules that in turn can trigger parallel trains of stimulation back into the brain. These modules may be built in software, VLSI firmware, or biological tissue as in vitro culture preparations or in vivo ectopic construct organoids...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28713233/the-role-of-the-interplay-between-stimulus-type-and-timing-in-explaining-bci-illiteracy-for-visual-p300-based-brain-computer-interfaces
#10
Roberta Carabalona
Visual P300-based Brain-Computer Interface (BCI) spellers enable communication or interaction with the environment by flashing elements in a matrix and exploiting consequent changes in end-user's brain activity. Despite research efforts, performance variability and BCI-illiteracy still are critical issues for real world applications. Moreover, there is a quite unaddressed kind of BCI-illiteracy, which becomes apparent when the same end-user operates BCI-spellers intended for different applications: our aim is to understand why some well performers can become BCI-illiterate depending on speller type...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28713232/detection-of-movement-related-cortical-potentials-from-eeg-using-constrained-ica-for-brain-computer-interface-applications
#11
Fatemeh Karimi, Jonathan Kofman, Natalie Mrachacz-Kersting, Dario Farina, Ning Jiang
The movement related cortical potential (MRCP), a slow cortical potential from the scalp electroencephalogram (EEG), has been used in real-time brain-computer-interface (BCI) systems designed for neurorehabilitation. Detecting MPCPs in real time with high accuracy and low latency is essential in these applications. In this study, we propose a new MRCP detection method based on constrained independent component analysis (cICA). The method was tested for MRCP detection during executed and imagined ankle dorsiflexion of 24 healthy participants, and compared with four commonly used spatial filters for MRCP detection in an offline experiment...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28711988/emotion-recognition-based-on-eeg-features-in-movie-clips-with-channel-selection
#12
Mehmet Siraç Özerdem, Hasan Polat
Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain-computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems...
July 15, 2017: Brain Informatics
https://www.readbyqxmd.com/read/28708963/a-qualitative-study-adopting-a-user-centered-approach-to-design-and-validate-a-brain-computer-interface-for-cognitive-rehabilitation-for-people-with-brain-injury
#13
Suzanne Martin, Elaine Armstrong, Eileen Thomson, Eloisa Vargiu, Marc Solà, Stefan Dauwalder, Felip Miralles, Jean Daly Lynn
Cognitive rehabilitation is established as a core intervention within rehabilitation programs following a traumatic brain injury (TBI). Digitally enabled assistive technologies offer opportunities for clinicians to increase remote access to rehabilitation supporting transition into home. Brain Computer Interface (BCI) systems can harness the residual abilities of individuals with limited function to gain control over computers through their brain waves. This paper presents an online cognitive rehabilitation application developed with therapists, to work remotely with people who have TBI, who will use BCI at home to engage in the therapy...
July 14, 2017: Assistive Technology: the Official Journal of RESNA
https://www.readbyqxmd.com/read/28706472/relevant-feature-integration-and-extraction-for-single-trial-motor-imagery-classification
#14
Lili Li, Guanghua Xu, Feng Zhang, Jun Xie, Min Li
Brain computer interfaces provide a novel channel for the communication between brain and output devices. The effectiveness of the brain computer interface is based on the classification accuracy of single trial brain signals. The common spatial pattern (CSP) algorithm is believed to be an effective algorithm for the classification of single trial brain signals. As the amplitude feature for spatial projection applied by this algorithm is based on a broad frequency bandpass filter (mainly 5-30 Hz) in which the frequency band is often selected by experience, the CSP is sensitive to noise and the influence of other irrelevant information in the selected broad frequency band...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28701939/behavioral-and-cortical-effects-during-attention-driven-brain-computer-interface-operations-in-spatial-neglect-a-feasibility-case-study
#15
Luca Tonin, Marco Pitteri, Robert Leeb, Huaijian Zhang, Emanuele Menegatti, Francesco Piccione, José Del R Millán
During the last years, several studies have suggested that Brain-Computer Interface (BCI) can play a critical role in the field of motor rehabilitation. In this case report, we aim to investigate the feasibility of a covert visuospatial attention (CVSA) driven BCI in three patients with left spatial neglect (SN). We hypothesize that such a BCI is able to detect attention task-specific brain patterns in SN patients and can induce significant changes in their abnormal cortical activity (α-power modulation, feature recruitment, and connectivity)...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28701938/improving-eeg-based-emotion-classification-using-conditional-transfer-learning
#16
Yuan-Pin Lin, Tzyy-Ping Jung
To overcome the individual differences, an accurate electroencephalogram (EEG)-based emotion-classification system requires a considerable amount of ecological calibration data for each individual, which is labor-intensive and time-consuming. Transfer learning (TL) has drawn increasing attention in the field of EEG signal mining in recent years. The TL leverages existing data collected from other people to build a model for a new individual with little calibration data. However, brute-force transfer to an individual (i...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28696536/an-emergency-call-system-for-patients-in-locked-in-state-using-an-ssvep-based-brain-switch
#17
Jeong-Hwan Lim, Yong-Wook Kim, Jun-Hak Lee, Kwang-Ok An, Han-Jeong Hwang, Ho-Seung Cha, Chang-Hee Han, Chang-Hwan Im
Patients in a locked-in state (LIS) due to severe neurological disorders such as amyotrophic lateral sclerosis (ALS) require seamless emergency care by their caregivers or guardians. However, it is a difficult job for the guardians to continuously monitor the patients' state, especially when direct communication is not possible. In the present study, we developed an emergency call system for such patients using a steady-state visual evoked potential (SSVEP)-based brain switch. Although there have been previous studies to implement SSVEP-based brain switch system, they have not been applied to patients in LIS, and thus their clinical value has not been validated...
July 11, 2017: Psychophysiology
https://www.readbyqxmd.com/read/28692997/automated-classification-and-removal-of-eeg-artifacts-with-svm-and-wavelet-ica
#18
Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Paul Cumming, Masahiro Iwahashi
Brain electrical activity recordings by electroencephalography (EEG) are often contaminated with signal artifacts. Procedures for automated removal of EEG artifacts are frequently sought for clinical diagnostics and brain computer interface (BCI) applications. In recent years, a combination of independent component analysis (ICA) and discrete wavelet transform (DWT) has been introduced as standard technique for EEG artifact removal. However, in performing the wavelet-ICA procedure, visual inspection or arbitrary thresholding may be required for identifying artifactual components in the EEG signal...
July 4, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28692996/effectiveness-evaluation-of-real-time-scalp-signal-separating-algorithm-on-near-infrared-spectroscopy-neurofeedback
#19
Wei Chun Ung, Tsukasa Funane, Takushige Katura, Hiroki Sato, Tong Boon Tang, Ahmad Fadzil Mohammad Hani, Masashi Kiguchi
Near-infrared spectroscopy (NIRS), one of the candidates to be used in a neurofeedback system or brain-computer interface (BCI), measures the brain activity by monitoring the changes in cerebral hemoglobin concentration. However, hemodynamic changes in the scalp may affect the NIRS signals. In order to remove the superficial signals when NIRS is used in a neurofeedback system or BCI, real-time processing is necessary. Real-time scalp signal separating (RT-SSS) algorithm, which is capable of separating the scalp-blood signals from NIRS signals obtained in real-time, may thus be applied...
July 4, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28690497/high-amplitude-eeg-motor-potential-during-repetitive-foot-movement-possible-use-and-challenges-for-futuristic-bcis-that-restore-mobility-after-spinal-cord-injury
#20
Aljoscha Thomschewski, Yvonne Höller, Peter Höller, Stefan Leis, Eugen Trinka
Recent advances in neuroprostheses provide us with promising ideas of how to improve the quality of life in people suffering from impaired motor functioning of upper and lower limbs. Especially for patients after spinal cord injury (SCI), futuristic devices that are controlled by thought via brain-computer interfaces (BCIs) might be of tremendous help in managing daily tasks and restoring at least some mobility. However, there are certain problems arising when trying to implement BCI technology especially in such a heterogenous patient group...
2017: Frontiers in Neuroscience
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