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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/28227842/phase-modulation-based-response-inhibition-outcome-prediction-in-translational-scenario-of-stop-signal-task
#5
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
#6
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/28227811/detection-of-movement-related-cortical-potential-effects-of-causal-vs-non-causal-processing
#7
Omid G Sani, Ricardo Chavarriaga, Mohammad B Shamsollahi, Jose Del R Millan, Omid G Sani, Ricardo Chavarriaga, Mohammad B Shamsollahi, Jose Del R Millan, Mohammad B Shamsollahi, Omid G Sani, Ricardo Chavarriaga, Jose R Del Millan
Movement Related Cortical Potentials (MRCP) have been the subject of numerous studies. They accompany many self-initiated movements and this makes them a good candidate for incorporation in BCI paradigms. In this work we propose a novel experimental protocol involving natural controlling of a computer mouse and based on EEG recordings from 5 subjects, show that it elicits MRCP. We also show the feasibility of online detection of MRCP by implementing a classification based detection framework. Additionally, we discuss the adverse effects of causality restriction on detection performance by implementing an additional offline approach relaxing those restrictions and comparing the results...
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/28227802/developing-a-one-channel-bci-system-using-a-dry-claw-like-electrode
#10
Xuhong Guo, Weihua Pei, Yijun Wang, Qiang Gui, He Zhang, Xiao Xing, Yong Huang, Hongda Chen, Ruicong Liu, Yuanyuan Liu, Xuhong Guo, Weihua Pei, Yijun Wang, Qiang Gui, He Zhang, Xiao Xing, Yong Huang, Hongda Chen, Ruicong Liu, Yuanyuan Liu, Hongda Chen, Yijun Wang, Weihua Pei, Yuanyuan Liu, Ruicong Liu, He Zhang, Qiang Gui, Xuhong Guo, Yong Huang, Xiao Xing
An eight-class SSVEP-based BCI system was designed and demonstrated in this study. To minimize the complexity of the traditional equipment and operation, only one work electrode was used. The work electrode was fabricated in our laboratory and designed as a claw-like structure with a diameter of 15 mm, featuring 8 small fingers of 4mm length and 2 mm diameter, and the weight was only 0.1g. The structure and elasticity can help the fingers pass through the hair and contact the scalp when placed on head. The electrode was capable to collect evoked brain activities such as steady-state visual evoked potentials (SSVEPs)...
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
#11
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/28227510/feasibility-of-an-ultra-low-power-digital-signal-processor-platform-as-a-basis-for-a-fully-implantable-brain-computer-interface-system
#12
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
#13
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
#14
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
#15
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/28227170/big-data-challenges-in-decoding-cortical-activity-in-a-human-with-quadriplegia-to-inform-a-brain-computer-interface
#16
David A Friedenberg, Chad E Bouton, Nicholas V Annetta, Nicholas Skomrock, Mingming Zhang, Michael Schwemmer, Marcia A Bockbrader, W Jerry Mysiw, Ali R Rezai, Herbert S Bresler, Gaurav Sharma, David A Friedenberg, Chad E Bouton, Nicholas V Annetta, Nicholas Skomrock, Mingming Zhang, Michael Schwemmer, Marcia A Bockbrader, W Jerry Mysiw, Ali R Rezai, Herbert S Bresler, Gaurav Sharma, Nicholas Skomrock, David A Friedenberg, Marcia A Bockbrader, Chad E Bouton, Ali R Rezai, Mingming Zhang, Herbert S Bresler, Gaurav Sharma, W Jerry Mysiw, Nicholas V Annetta, Michael Schwemmer
Recent advances in Brain Computer Interfaces (BCIs) have created hope that one day paralyzed patients will be able to regain control of their paralyzed limbs. As part of an ongoing clinical study, we have implanted a 96-electrode Utah array in the motor cortex of a paralyzed human. The array generates almost 3 million data points from the brain every second. This presents several big data challenges towards developing algorithms that should not only process the data in real-time (for the BCI to be responsive) but are also robust to temporal variations and non-stationarities in the sensor data...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227152/designing-a-hands-on-brain-computer-interface-laboratory-course
#17
Bahar Khalighinejad, Laura Kathleen Long, Nima Mesgarani, Bahar Khalighinejad, Laura Kathleen Long, Nima Mesgarani, Bahar Khalighinejad, Laura Kathleen Long, Nima Mesgarani
Devices and systems that interact with the brain have become a growing field of research and development in recent years. Engineering students are well positioned to contribute to both hardware development and signal analysis techniques in this field. However, this area has been left out of most engineering curricula. We developed an electroencephalography (EEG) based brain computer interface (BCI) laboratory course to educate students through hands-on experiments. The course is offered jointly by the Biomedical Engineering, Electrical Engineering, and Computer Science Departments of Columbia University in the City of New York and is open to senior undergraduate and graduate students...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227095/a-small-portable-battery-powered-brain-computer-interface-system-for-motor-rehabilitation
#18
Colin M McCrimmon, Ming Wang, Lucas Silva Lopes, Po T Wang, Alireza Karimi-Bidhendi, Charles Y Liu, Payam Heydari, Zoran Nenadic, An H Do, Colin M McCrimmon, Ming Wang, Lucas Silva Lopes, Po T Wang, Alireza Karimi-Bidhendi, Charles Y Liu, Payam Heydari, Zoran Nenadic, An H Do, Ming Wang, An H Do, Zoran Nenadic, Colin M McCrimmon, Alireza Karimi-Bidhendi, Lucas Silva Lopes, Po T Wang, Charles Y Liu, Payam Heydari
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/28227094/mutual-information-based-feature-selection-for-low-cost-bcis-based-on-motor-imagery
#19
L Schiatti, L Faes, J Tessadori, G Barresi, L Mattos, L Schiatti, L Faes, J Tessadori, G Barresi, L Mattos, G Barresi, L Mattos, J Tessadori, L Faes, L Schiatti
In the present study a feature selection algorithm based on mutual information (MI) was applied to electro-encephalographic (EEG) data acquired during three different motor imagery tasks from two dataset: Dataset I from BCI Competition IV including full scalp recordings from four subjects, and new data recorded from three subjects using the popular low-cost Emotiv EPOC EEG headset. The aim was to evaluate optimal channels and band-power (BP) features for motor imagery tasks discrimination, in order to assess the feasibility of a portable low-cost motor imagery based Brain-Computer Interface (BCI) system...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227093/applicability-of-ssvep-based-brain-computer-interfaces-for-robot-navigation-in-real-environments
#20
Christina Farmaki, Georgios Christodoulakis, Vangelis Sakkalis, Christina Farmaki, Georgios Christodoulakis, Vangelis Sakkalis, Vangelis Sakkalis, Christina Farmaki, Georgios Christodoulakis
Brain-computer interfaces have been extensively studied and used in order to aid patients suffering from neuromuscular diseases to communicate and control the surrounding environment. Steady-state visual evoked potentials (SSVEP) constitute a very popular BCI stimulation protocol, due to their efficiency and quick response time. In this study, we developed a SSVEP-based BCI along with a low-cost custom radio-controlled robot-car providing live video feedback from a wireless camera mounted on the robot, serving as our testbed...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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