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Brain-Computer interface

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https://www.readbyqxmd.com/read/28325008/feasibility-of-an-ultra-low-power-digital-signal-processor-platform-as-a-basis-for-a-fully-implantable-brain-computer-interface-system
#1
Po T Wang, Keulanna Gandasetiawan, Colin M McCrimmon, Alireza Karimi-Bidhendi, Charles Y Liu, Payam Heydari, Zoran Nenadic, An H Do
A fully implantable brain-computer interface (BCI) can be a practical tool to restore independence to those affected by spinal cord injury. We envision that such a BCI system will invasively acquire brain signals (e.g. electrocorticogram) and translate them into control commands for external prostheses. The feasibility of such a system was tested by implementing its benchtop analogue, centered around a commercial, ultra-low power (ULP) digital signal processor (DSP, TMS320C5517, Texas Instruments). A suite of signal processing and BCI algorithms, including (de)multiplexing, Fast Fourier Transform, power spectral density, principal component analysis, linear discriminant analysis, Bayes rule, and finite state machine was implemented and tested in the DSP...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324971/a-small-portable-battery-powered-brain-computer-interface-system-for-motor-rehabilitation
#2
Colin M McCrimmon, Ming Wang, Lucas Silva Lopes, Po T Wang, Alireza Karimi-Bidhendi, Charles Y Liu, Payam Heydari, Zoran Nenadic, An H Do
Motor rehabilitation using brain-computer interface (BCI) systems may facilitate functional recovery in individuals after stroke or spinal cord injury. Nevertheless, these systems are typically ill-suited for widespread adoption due to their size, cost, and complexity. In this paper, a small, portable, and extremely cost-efficient (<;$200) BCI system has been developed using a custom electroencephalographic (EEG) amplifier array, and a commercial microcontroller and touchscreen. The system's performance was tested using a movement-related BCI task in 3 able-bodied subjects with minimal previous BCI experience...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28320670/bci-use-and-its-relation-to-adaptation-in-cortical-networks
#3
Kaitlyn Casimo, Kurt E Weaver, Jeremiah Wander, Jeffrey G Ojemann
Brain-computer interfaces (BCIs) carry great potential in the treatment of motor impairments. As a new motor output, BCIs interface with the native motor system, but acquisition of BCI proficiency requires a degree of learning to integrate this new function. In this review, we discuss how BCI designs often take advantage of the brain's motor system infrastructure as sources of command signals. We highlight a growing body of literature examining how this approach leads to changes in activity across cortex, including beyond motor regions, as a result of learning the new skill of BCI control...
March 13, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28316617/an-efficient-framework-for-eeg-analysis-with-application-to-hybrid-brain-computer-interfaces-based-on-motor-imagery-and-p300
#4
Jinyi Long, Jue Wang, Tianyou Yu
The hybrid brain computer interface (BCI) based on motor imagery (MI) and P300 has been a preferred strategy aiming to improve the detection performance through combining the features of each. However, current methods used for combining these two modalities optimize them separately, which does not result in optimal performance. Here, we present an efficient framework to optimize them together by concatenating the features of MI and P300 in a block diagonal form. Then a linear classifier under a dual spectral norm regularizer is applied to the combined features...
2017: Computational Intelligence and Neuroscience
https://www.readbyqxmd.com/read/28298888/reaching-and-grasping-a-glass-of-water-by-locked-in-als-patients-through-a-bci-controlled-humanoid-robot
#5
Rossella Spataro, Antonio Chella, Brendan Allison, Marcello Giardina, Rosario Sorbello, Salvatore Tramonte, Christoph Guger, Vincenzo La Bella
Locked-in Amyotrophic Lateral Sclerosis (ALS) patients are fully dependent on caregivers for any daily need. At this stage, basic communication and environmental control may not be possible even with commonly used augmentative and alternative communication devices. Brain Computer Interface (BCI) technology allows users to modulate brain activity for communication and control of machines and devices, without requiring a motor control. In the last several years, numerous articles have described how persons with ALS could effectively use BCIs for different goals, usually spelling...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28294109/goal-recognition-based-adaptive-brain-computer-interface-for-navigating-immersive-robotic-systems
#6
Mohammad Abu-Alqumsan, Felix Ebert, Angelika Peer
OBJECTIVE: This work proposes principled strategies for self-adaptations in EEG-based Brain-computer interfaces (BCIs) as a way out of the bandwidth bottleneck resulting from the considerable mismatch between the low-bandwidth interface and the bandwidth-hungry application, and a way to enable fluent and intuitive interaction in embodiment systems. The main focus is laid upon inferring the hidden target goals of users while navigating in a remote environment as a basis for possible adaptations...
March 15, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28293184/evaluation-of-a-dry-eeg-system-for-application-of-passive-brain-computer-interfaces-in-autonomous-driving
#7
Thorsten O Zander, Lena M Andreessen, Angela Berg, Maurice Bleuel, Juliane Pawlitzki, Lars Zawallich, Laurens R Krol, Klaus Gramann
We tested the applicability and signal quality of a 16 channel dry electroencephalography (EEG) system in a laboratory environment and in a car under controlled, realistic conditions. The aim of our investigation was an estimation how well a passive Brain-Computer Interface (pBCI) can work in an autonomous driving scenario. The evaluation considered speed and accuracy of self-applicability by an untrained person, quality of recorded EEG data, shifts of electrode positions on the head after driving-related movements, usability, and complexity of the system as such and wearing comfort over time...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28293163/an-fpga-platform-for-real-time-simulation-of-spiking-neuronal-networks
#8
Danilo Pani, Paolo Meloni, Giuseppe Tuveri, Francesca Palumbo, Paolo Massobrio, Luigi Raffo
In the last years, the idea to dynamically interface biological neurons with artificial ones has become more and more urgent. The reason is essentially due to the design of innovative neuroprostheses where biological cell assemblies of the brain can be substituted by artificial ones. For closed-loop experiments with biological neuronal networks interfaced with in silico modeled networks, several technological challenges need to be faced, from the low-level interfacing between the living tissue and the computational model to the implementation of the latter in a suitable form for real-time processing...
2017: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/28291737/advantages-of-eeg-phase-patterns-for-the-detection-of-gait-intention-in-healthy-and-stroke-subjects
#9
Andreea Ioana Sburlea, Luis Montesano, Javier Minguez
OBJECTIVE: One use of EEG-based brain-computer interfaces (BCIs) in rehabilitation is the detection of movement intention. In this paper we investigate for the first time the instantaneous phase of movement related cortical potential (MRCP) and its application to the detection of gait intention. APPROACH: We demonstrate the utility of MRCP phase in two independent datasets, in which 10 healthy subjects and 9 chronic stroke patients executed a self-initiated gait task in three sessions...
March 14, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28288824/switching-markov-decoders-for-asynchronous-trajectory-reconstruction-from-ecog-signals-in-monkeys-for-bci-applications
#10
Marie-Caroline Schaeffer, Tetiana Aksenova
Brain-Computer Interfaces (BCIs) are systems which translate brain neural activity into commands for external devices. BCI users generally alternate between No-Control (NC) and Intentional Control (IC) periods. NC/IC discrimination is crucial for clinical BCIs, particularly when they provide neural control over complex effectors such as exoskeletons. Numerous BCI decoders focus on the estimation of continuously-valued limb trajectories from neural signals. The integration of NC support into continuous decoders is investigated in the present article...
March 10, 2017: Journal of Physiology, Paris
https://www.readbyqxmd.com/read/28287076/improving-zero-training-brain-computer-interfaces-by-mixing-model-estimators
#11
Thibault Verhoeven, David Hübner, Michael Tangermann, Klaus-Robert Mueller, Joni Dambre, Pieter-Jan Kindermans
OBJECTIVE: Brain-computer interfaces (BCI) based on event-related potentials (ERP) incorporate a decoder to classify recorded brain signals and subsequently select a control signal that drives a computer application. Standard supervised BCI decoders require a tedious calibration procedure prior to every session. Several unsupervised classification methods have been proposed that tune the decoder during actual use and as such omit this calibration. Each of these methods has its own strengths and weaknesses...
March 13, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28286317/7t-fmri-faster-temporal-resolution-yields-optimal-bold-sensitivity-for-functional-network-imaging-specifically-at-high-spatial-resolution
#12
Peter E Yoo, Sam E John, Shawna Farquharson, Jon O Cleary, Yan T Wong, Amanda Ng, Claire Mulcahy, David B Grayden, Roger J Ordidge, Nicholas L Opie, Terence J O'Brien, Thomas J Oxley, Bradford A Moffat
Recent developments in accelerated imaging methods allow faster acquisition of high spatial resolution images. This could improve the applications of functional magnetic resonance imaging at 7 T (7T-fMRI), such as neurosurgical planning and Brain Computer Interfaces (BCIs). However, increasing the spatial and temporal resolution will both lead to signal-to-noise ratio (SNR) losses due to decreased signal intensity and T1-relaxation effect, respectively. This could potentially offset the SNR efficiency gains made with increasing temporal resolution...
March 7, 2017: NeuroImage
https://www.readbyqxmd.com/read/28286237/retrospectively-supervised-click-decoder-calibration-for-self-calibrating-point-and-click-brain-computer-interfaces
#13
Beata Jarosiewicz, Anish A Sarma, Jad Saab, Brian Franco, Sydney S Cash, Emad N Eskandar, Leigh R Hochberg
Brain-computer interfaces (BCIs) aim to restore independence to people with severe motor disabilities by allowing control of acursor on a computer screen or other effectors with neural activity. However, physiological and/or recording-related nonstationarities in neural signals can limit long-term decoding stability, and it would be tedious for users to pause use of the BCI whenever neural control degrades to perform decoder recalibration routines. We recently demonstrated that a kinematic decoder (i.e. a decoder that controls cursor movement) can be recalibrated using data acquired during practical point-and-click control of the BCI by retrospectively inferring users' intended movement directions based on their subsequent selections...
March 8, 2017: Journal of Physiology, Paris
https://www.readbyqxmd.com/read/28278476/review-human-intracortical-recording-and-neural-decoding-for-brain-computer-interfaces
#14
David M Brandman, Sydney S Cash, Leigh R Hochberg
Brain Computer Interfaces (BCIs) use neural information recorded from the brain for voluntary control of external devices. The development of BCI systems has largely focused on improving functional independence for individuals with severe motor impairments, including providing tools for communication and mobility. In this review, we describe recent advances in intracortical BCI technology and provide potential directions for further research.
March 2, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28275048/brain-machine-interfaces-from-basic-science-to-neuroprostheses-and-neurorehabilitation
#15
REVIEW
Mikhail A Lebedev, Miguel A L Nicolelis
Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between living brains and artificial actuators. Although theoretical propositions and some proof of concept experiments on directly linking the brains with machines date back to the early 1960s, BMI research only took off in earnest at the end of the 1990s, when this approach became intimately linked to new neurophysiological methods for sampling large-scale brain activity...
April 2017: Physiological Reviews
https://www.readbyqxmd.com/read/28269716/motor-imagery-based-brain-computer-interface-using-transform-domain-features
#16
Ahmed M Elbaz, Ahmed T Ahmed, Ayman M Mohamed, Mohamed A Oransa, Khaled S Sayed, Ayman M Eldeib
Brain Computer Interface (BCI) is a channel of communication between the human brain and an external device through brain electrical activity. In this paper, we extracted different features to boost the classification accuracy as well as the mutual information of BCI systems. The extracted features include the magnitude of the discrete Fourier transform and the wavelet coefficients for the EEG signals in addition to distance series values and invariant moments calculated for the reconstructed phase space of the EEG measurements...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269590/time-frequency-joint-coding-method-for-boosting-information-transfer-rate-in-an-ssvep-based-bci-system
#17
Ke Lin, Yijun Wang, Xiaorong Gao
Steady-State Visual Evoked Potential (SSVEP) based Brain-Computer Interface (BCI) system is an important BCI modality. It has advantages such as ease of use, little training and high Information Transfer Rate (ITR). Traditional SSVEP based BCI systems are based on the Frequency Division Multiple Access (FDMA) approach in telecommunications. Recently, Time Division Multiple Access (TDMA) was also introduced to SSVEP based BCI to enhance the system performance. This study designed a new time-frequency joint coding method to utilize the information coding from both time and frequency domains...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269589/high-performance-wearable-two-channel-hybrid-bci-system-with-eye-closure-assist
#18
Yubing Jiang, Hyeonseok Lee, Gang Li, Wan-Young Chung
Generally, eye closure (EC) and eye opening (EO)-based alpha blocking has widely recognized advantages, such as being easy to use, requiring little user training, while motor imagery (MI) is difficult for some users to have concrete feelings. This study presents a hybrid brain-computer interface (BCI) combining MI and EC strategies - such an approach aims to overcome some disadvantages of MI-based BCI, improve the performance and universality of the BCI. The EC/EO is employed to control the machine to switch in different states including forward, stop, changing direction motions, while the MI is used to control the machine to turn left or right for 90° by imagining the hands grasp motions when the system is switched into "changing direction" state...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269588/maximum-entropy-based-common-spatial-patterns-for-motor-imagery-classification
#19
Syed Salman Ali, Lei Zhang
The common spatial pattern (CSP) is extensively used to extract discriminative feature from raw Electroencephalography (EEG) signals for motor imagery classification. The CSP is a statistical signal processing technique, which relies on sample based covariance matrix estimation to give discriminative information from raw EEG signals. The sample based estimation of covariance matrix becomes a problem when the number of training samples is limited, which causes the performance of CSP based brain computer interface (BCI) to degrade significantly...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28269587/investigating-motor-imagery-tasks-by-their-neural-effects-a-case-study
#20
I E Nicolae, M M C Stefan, B Hurezeanu, D D Taralunga, R Strungaru, T M Vasile, O A Bajenaru, G M Ungureanu
Motor imagery, one of the first investigated neural process for Brain-Computer Interfaces (BCIs) still provides a great challenge nowadays. Aiming a better and more accurate control, multiple researches have been conducted by the scientific community. Nevertheless, there is still no robust and confident application developed. In order to augment the potential referring to motor imagery, and to attract user's interest, we propose multiple motor imagery tasks in combination with different visual or auditory stimuli...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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