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Brain computer interface

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https://www.readbyqxmd.com/read/28227980/motor-imagery-based-brain-computer-interface-using-transform-domain-features
#1
Ahmed M Elbaz, Ahmed T Ahmed, Ayman M Mohamed, Mohamed A Oransa, Khaled S Sayed, Ayman M Eldeib, Ahmed M Elbaz, Ahmed T Ahmed, Ayman M Mohamed, Mohamed A Oransa, Khaled S Sayed, Ayman M Eldeib, Mohamed A Oransa, Khaled S Sayed, Ayman M Mohamed, Ahmed T Ahmed, Ahmed M Elbaz, 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/28227846/time-frequency-joint-coding-method-for-boosting-information-transfer-rate-in-an-ssvep-based-bci-system
#2
Ke Lin, Yijun Wang, Xiaorong Gao, Ke Lin, Yijun Wang, Xiaorong Gao, Yijun Wang, Xiaorong Gao, Ke Lin
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/28227845/high-performance-wearable-two-channel-hybrid-bci-system-with-eye-closure-assist
#3
Yubing Jiang, Hyeonseok Lee, Gang Li, Wan-Young Chung, Yubing Jiang, Hyeonseok Lee, Gang Li, Wan-Young Chung, Wan-Young Chung, Gang Li, Hyeonseok Lee, Yubing Jiang
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/28227844/maximum-entropy-based-common-spatial-patterns-for-motor-imagery-classification
#4
Syed Salman Ali, Lei Zhang, Syed Salman Ali, Lei Zhang, 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/28227843/investigating-motor-imagery-tasks-by-their-neural-effects-a-case-study
#5
I E Nicolae, M M C Stefan, B Hurezeanu, D D Taralunga, R Strungaru, T M Vasile, O A Bajenaru, G M Ungureanu, I E Nicolae, M M C Stefan, B Hurezeanu, D D Taralunga, R Strungaru, T M Vasile, O A Bajenaru, G M Ungureanu, B Hurezeanu, R Strungaru, G M Ungureanu, O A Bajenaru, D D Taralunga, T M Vasile, I E Nicolae, M M C Stefan
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
https://www.readbyqxmd.com/read/28227842/phase-modulation-based-response-inhibition-outcome-prediction-in-translational-scenario-of-stop-signal-task
#6
Rupesh Kumar Chikara, Li-Wei Ko, Rupesh Kumar Chikara, Li-Wei Ko, Li-Wei Ko, Rupesh Kumar Chikara
In this paper, a method is proposed to predict the resting-state outcomes of participants based on their electroencephalogram (EEG) signals recorded before the successful /unsuccessful response inhibition. The motivation of this study is to enhance the shooter performance for shooting the target, when their EEG patterns show that they are ready. This method can be used in brain-computer interface (BCI) system. In this study, multi-channel EEG from twenty participants are collected by the electrodes placed at different scalp locations in resting-state time...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227841/comparing-eeg-its-time-derivative-and-their-joint-use-as-features-in-a-bci-for-2-d-pointer-control
#7
Dimitrios Andreou, Riccardo Poli, Dimitrios Andreou, Riccardo Poli, Riccardo Poli, Dimitrios Andreou
Efficient and accurate classification of event related potentials is a core task in brain-computer interfaces (BCI). This is normally obtained by first extracting features from the voltage amplitudes recorded via EEG at different channels and then feeding them into a classifier. In this paper we evaluate the relative benefits of using the first order temporal derivatives of the EEG signals, not the EEG signals themselves, as inputs to the BCI: an area that has not been thoroughly examined. Specifically, we compare the classification performance of features extracted from the first derivative, with those derived from the amplitude, as well as their combination using data from a P300-based BCI mouse...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227809/feature-domain-specific-movement-intention-detection-for-stroke-rehabilitation-with-brain-computer-interfaces
#8
J T Hadsund, M B Sorensen, A C Royo, I K Niazi, H Rovsing, C Rovsing, M Jochumsen, J T Hadsund, M B Sorensen, A C Royo, I K Niazi, H Rovsing, C Rovsing, M Jochumsen, H Rovsing, M Jochumsen, M B Sorensen, C Rovsing, A C Royo, I K Niazi, J T Hadsund
Brain-computer interface (BCI) driven electrical stimulation has been proposed for neuromodulation for stroke rehabilitation by pairing intentions to move with somatosensory feedback from electrical stimulation. Movement intentions have been detected in several studies using different techniques, with temporal and spectral features being the most common. A few studies have compared temporal and spectral features, but conflicting results have been reported. In this study, the aim was to investigate if complexity measures can be used for movement intention detection and to compare the detection performance based on features extracted from three different domains (time, frequency and complexity) from single-trial EEG...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227807/ecog-data-analyses-to-inform-closed-loop-bci-experiments-for-speech-based-prosthetic-applications
#9
Tejaswy Pailla, Werner Jiang, Benjamin Dichter, Edward F Chang, Vikash Gilja, Tejaswy Pailla, Werner Jiang, Benjamin Dichter, Edward F Chang, Vikash Gilja, Vikash Gilja, Werner Jiang, Tejaswy Pailla, Benjamin Dichter, Edward F Chang
Brain Computer Interfaces (BCIs) assist individuals with motor disabilities by enabling them to control prosthetic devices with their neural activity. Performance of closed-loop BCI systems can be improved by using design strategies that leverage structured and task-relevant neural activity. We use data from high density electrocorticography (ECoG) grids implanted in three subjects to study sensory-motor activity during an instructed speech task in which the subjects vocalized three cardinal vowel phonemes...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227805/modular-multipin-electrodes-for-comfortable-dry-eeg
#10
P Fiedler, D Strohmeier, A Hunold, S Griebel, R Muhle, M Schreiber, P Pedrosa, B Vasconcelos, C Fonseca, F Vaz, J Haueisen, P Fiedler, D Strohmeier, A Hunold, S Griebel, R Muhle, M Schreiber, P Pedrosa, B Vasconcelos, C Fonseca, F Vaz, J Haueisen, P Pedrosa, F Vaz, P Fiedler, B Vasconcelos, J Haueisen, M Schreiber, R Muhle, C Fonseca, S Griebel, D Strohmeier, A Hunold
Electrode and cap concepts for continuous and ubiquitous monitoring of brain activity will open up new fields of application and contribute to increased use of electroencephalography (EEG) in clinical routine, neurosciences, brain-computer-interfacing and out-of-the-lab monitoring. However, mobile and unobtrusive applications are currently hindered by the lack of applicable convenient and reliable electrode and cap systems. We propose a novel modular electrode concept based on a flexible polymer substrate, coated with electrically conductive metallic films...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227803/ear-eeg-allows-extraction-of-neural-responses-in-challenging-listening-scenarios-a-future-technology-for-hearing-aids
#11
L Fiedler, J Obleser, T Lunner, C Graversen, L Fiedler, J Obleser, T Lunner, C Graversen, J Obleser, C Graversen, L Fiedler, T Lunner
Advances in brain-computer interface research have recently empowered the development of wearable sensors to record mobile electroencephalography (EEG) as an unobtrusive and easy-to-use alternative to conventional scalp EEG. One such mobile solution is to record EEG from the ear canal, which has been validated for auditory steady state responses and discrete event related potentials (ERPs). However, it is still under discussion where to place recording and reference electrodes to capture best responses to auditory stimuli...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227795/simplified-realistic-human-head-model-for-simulating-tumor-treating-fields-ttfields
#12
Cornelia Wenger, Ze'ev Bomzon, Ricardo Salvador, Peter J Basser, Pedro C Miranda, Cornelia Wenger, Ze'ev Bomzon, Ricardo Salvador, Peter J Basser, Pedro C Miranda, Peter J Basser, Pedro C Miranda, Cornelia Wenger, Ricardo Salvador, Ze'ev Bomzon
Tumor Treating Fields (TTFields) are alternating electric fields in the intermediate frequency range (100-300 kHz) of low-intensity (1-3 V/cm). TTFields are an anti-mitotic treatment against solid tumors, which are approved for Glioblastoma Multiforme (GBM) patients. These electric fields are induced non-invasively by transducer arrays placed directly on the patient's scalp. Cell culture experiments showed that treatment efficacy is dependent on the induced field intensity. In clinical practice, a software called NovoTal(TM) uses head measurements to estimate the optimal array placement to maximize the electric field delivery to the tumor...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227579/implementation-of-a-control-system-for-a-power-wheelchair-with-induction-of-a-%C3%AE-%C3%AE-ratio-by-visual-feedback
#13
Yudai Iida, Ryota Horie, Yudai Iida, Ryota Horie, Ryota Horie, Yudai Iida
Techniques using electroencephalography (EEG)-based brain computer interfaces (BCIs) have been developed and are eagerly anticipated as novel interfaces for controlling power wheelchairs. In addition to the BCIs, smart glass technology has been developed. In our previous study, we propose a prototype of an intuitive control system for a power wheelchair; this system comprises a simple EEG recorder, smart glass, and a microcomputer. Using this system, the power wheelchair moves straight ahead when a user concentrates, stops when the user blinks, and turns left or right when the user tilt his/her neck to the left or right, respectively...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227553/transfer-learning-with-large-scale-data-in-brain-computer-interfaces
#14
Chun-Shu Wei, Yuan-Pin Lin, Yu-Te Wang, Chin-Teng Lin, Tzyy-Ping Jung, Chun-Shu Wei, Yuan-Pin Lin, Yu-Te Wang, Chin-Teng Lin, Tzyy-Ping Jung, Yuan-Pin Lin, Yu-Te Wang, Tzyy-Ping Jung, Chin-Teng Lin, Chun-Shu Wei
Human variability in electroencephalogram (EEG) poses significant challenges for developing practical real-world applications of brain-computer interfaces (BCIs). The intuitive solution of collecting sufficient user-specific training/calibration data can be very labor-intensive and time-consuming, hindering the practicability of BCIs. To address this problem, transfer learning (TL), which leverages existing data from other sessions or subjects, has recently been adopted by the BCI community to build a BCI for a new user with limited calibration data...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227525/multi-direction-hand-movement-classification-using-eeg-based-source-space-analysis
#15
Vikram Shenoy Handiru, A P Vinod, Cuntai Guan, Vikram Shenoy Handiru, A P Vinod, Cuntai Guan, Vikram Shenoy Handiru, Cuntai Guan, A P Vinod
Recent advances in the brain-computer interfaces (BCIs) have demonstrated the inference of movement related activity using non-invasive EEG. However, most of the sensorspace approaches that study sensorimotor rhythms using EEG do not reveal the underlying neurophysiological phenomenon while executing or imagining the movement with finer control. Therefore, there is a need to examine feature extraction techniques in the cortical source space which can provide more information about the task compared to sensor-space...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227510/feasibility-of-an-ultra-low-power-digital-signal-processor-platform-as-a-basis-for-a-fully-implantable-brain-computer-interface-system
#16
Po T Wang, Keulanna Gandasetiawan, Colin M McCrimmon, Alireza Karimi-Bidhendi, Charles Y Liu, Payam Heydari, Zoran Nenadic, An H Do, Po T Wang, Keulanna Gandasetiawan, Colin M McCrimmon, Alireza Karimi-Bidhendi, Charles Y Liu, Payam Heydari, Zoran Nenadic, An H Do, An H Do, Zoran Nenadic, Colin M McCrimmon, Keulanna Gandasetiawan, Payam Heydari, Alireza Karimi-Bidhendi, Po T Wang, Charles Y Liu
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/28227496/movement-imagery-classification-in-emotiv-cap-based-system-by-nai%C3%AC-ve-bayes
#17
Vinicius N Stock, Alexandre Balbinot, Vinicius N Stock, Alexandre Balbinot, Vinicius N Stock, Alexandre Balbinot
Brain-computer interfaces (BCI) provide means of communications and control, in assistive technology, which do not require motor activity from the user. The goal of this study is to promote classification of two types of imaginary movements, left and right hands, in an EMOTIV cap based system, using the Naïve Bayes classifier. A preliminary analysis with respect to results obtained by other experiments in this field is also conducted. Processing of the electroencephalography (EEG) signals is done applying Common Spatial Pattern filters...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227402/simplified-eeg-inverse-solution-for-bci-real-time-implementation
#18
L Duque-Munoz, F Vargas, J D Lopez, L Duque-Munoz, F Vargas, J D Lopez, F Vargas, L Duque-Munoz, J D Lopez
EEG brain imaging has become a promising approach in Brain-computer interface applications. However, accurate reconstruction of active regions and computational burden are still open issues. In this paper, we propose to use a simplified forward model that includes the reduction of the cortical dipoles based on Brodmann areas together with state-of-the-art EEG brain imaging techniques. With this approach the well known Beamformers and Greedy Search inverse solutions become feasible for real-time implementation, while guaranteeing lower localization error than previous approaches used in BCI...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227323/estimation-and-modeling-of-eeg-amplitude-temporal-characteristics-using-a-marked-point-process-approach
#19
Carlos A Loza, Jose C Principe, Carlos A Loza, Jose C Principe, Carlos A Loza, Jose C Principe
We propose a novel interpretation of single channel Electroencephalogram (EEG) traces based on the transient nature of encoded processes in the brain. In particular, the proposed framework models EEG as the output of the noisy addition of temporal, reoccurring, transient patterns known as phasic events. This is not only neurophysiologically sound, but it also provides additional information that classical EEG analysis often disregards. Furthermore, by utilizing sparse decomposition techniques, it is possible to obtain amplitude and timing that is further modeled using estimation and fitting techniques...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227194/exploring-neuro-muscular-synergies-of-reaching-movements-with-unified-independent-component-analysis
#20
Fiorenzo Artoni, Elvira Pirondini, Alessandro Panarese, Silvestre Micera, Fiorenzo Artoni, Elvira Pirondini, Alessandro Panarese, Silvestre Micera, Elvira Pirondini, Alessandro Panarese, Fiorenzo Artoni, Silvestro Micera
The coordinated recruitment of group of muscles through muscles synergies is known to simplify the control of movements. However, how and to what extent such control scheme is encoded at a cortical level is poorly understood. So far, electroencephalography (EEG) and electromyography (EMG) have been used, separately, to investigate the cortical regions of the human brain which may be involved in activating muscle synergies. Here we aim at extending these results by looking for a hierarchical relationship between cortical and muscular sources of activity (neuro-muscular synergies) with a unified analysis of independent components (IC) simultaneously extracted from both EEG and EMG signals...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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