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Brain machine interface

Michael Lührs, Rainer Goebel
Turbo-Satori is a neurofeedback and brain-computer interface (BCI) toolbox for real-time functional near-infrared spectroscopy (fNIRS). It incorporates multiple pipelines from real-time preprocessing and analysis to neurofeedback and BCI applications. The toolbox is designed with a focus in usability, enabling a fast setup and execution of real-time experiments. Turbo-Satori uses an incremental recursive least-squares procedure for real-time general linear model calculation and support vector machine classifiers for advanced BCI applications...
October 2017: Neurophotonics
Aya Khalaf, Matthew Sybeldon, Ervin Sejdic, Murat Akcakaya
BACKGROUND: Functional transcranial Doppler (fTCD) is an ultrasound based neuroimaging technique used to assess neural activation that occurs during a cognitive task through measuring velocity of cerebral blood flow. NEW METHOD: The objective of this paper is to investigate the feasibility of a 2-class and 3-class real-time BCI based on blood flow velocity in left and right middle cerebral arteries in response to mental rotation and word generation tasks. Statistical features based on a five-level wavelet decomposition were extracted from the fTCD signals...
October 7, 2017: Journal of Neuroscience Methods
N F Ramsey, E Salari, E J Aarnoutse, M J Vansteensel, M B Bleichner, Z V Freudenburg
For people who cannot communicate due to severe paralysis or involuntary movements, technology that decodes intended speech from the brain may offer an alternative means of communication. If decoding proves to be feasible, intracranial Brain-Computer Interface systems can be developed which are designed to translate decoded speech into computer generated speech or to instructions for controlling assistive devices. Recent advances suggest that such decoding may be feasible from sensorimotor cortex, but it is not clear how this challenge can be approached best...
October 6, 2017: NeuroImage
David Lee, Sang-Hoon Park, Sang-Goog Lee
In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them...
October 7, 2017: Sensors
Hong Zhang, Yaoru Sun, Jie Li, Fang Wang, Zijian Wang
Motor imagery is widely used in the brain-computer interface (BCI) systems that can help people actively control devices to directly communicate with the external world, but its training and performance effect is usually poor for normal people. To improve operators' BCI performances, here we proposed a novel paradigm, which combined the covert verb reading in the traditional motor imagery paradigm. In our proposed paradigm, participants were asked to covertly read the presented verbs during imagining right hand or foot movements referred by those verbs...
October 4, 2017: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Rihui Li, Thomas Potter, Weitian Huang, Yingchun Zhang
Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic applications. A desirable BCI system should be portable, minimally invasive, and feature high classification accuracy and efficiency. As two commonly used non-invasive brain imaging modalities, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) BCI system have often been incorporated in the development of hybrid BCI systems, largely due to their complimentary properties. In this study, we aimed to investigate whether the early temporal information extracted from singular EEG and fNIRS channels on each hemisphere can be used to enhance the accuracy and efficiency of a hybrid EEG-fNIRS BCI system...
2017: Frontiers in Human Neuroscience
Sonia Todorova, Valérie Ventura
Decoding in the context of brain-machine interface is a prediction problem, with the aim of retrieving the most accurate kinematic predictions attainable from the available neural signals. While selecting models that reduce the prediction error is done to various degrees, decoding has not received the attention that the fields of statistics and machine learning have lavished on the prediction problem in the past two decades. Here, we take a more systematic approach to the decoding prediction problem and search for risk-optimized reverse regression, optimal linear estimation (OLE), and Kalman filter models within a large model space composed of several nonlinear transformations of neural spike counts at multiple temporal lags...
September 28, 2017: Neural Computation
Mads Jochumsen, Cecilie Rovsing, Helene Rovsing, Imran Khan Niazi, Kim Dremstrup, Ernest Nlandu Kamavuako
Detection of single-trial movement intentions from EEG is paramount for brain-computer interfacing in neurorehabilitation. These movement intentions contain task-related information and if this is decoded, the neurorehabilitation could potentially be optimized. The aim of this study was to classify single-trial movement intentions associated with two levels of force and speed and three different grasp types using EEG rhythms and components of the movement-related cortical potential (MRCP) as features. The feature importance was used to estimate encoding of discriminative information...
2017: Computational Intelligence and Neuroscience
Atsuko Nishimoto, Michiyuki Kawakami, Toshiyuki Fujiwara, Miho Hiramoto, Kaoru Honaga, Kaoru Abe, Katsuhiro Mizuno, Junichi Ushiba, Meigen Liu
OBJECTIVE: Brain-machine interface training was developed for upper-extremity rehabilitation for patients with severe hemiparesis. Its clinical application, however, has been limited because of its lack of feasibility in real-world rehabilitation settings. We developed a new compact task-specific brain-machine interface system that enables task-specific training, including reach-and-grasp tasks, and studied its clinical feasibility and effectiveness for upper-extremity motor paralysis in patients with stroke...
September 26, 2017: Journal of Rehabilitation Medicine
Zhijun Zhang, Yongqian Huang, Siyuan Chen, Jun Qu, Xin Pan, Tianyou Yu, Yuanqing Li
In this study, an intention-driven semi-autonomous intelligent robotic (ID-SIR) system is designed and developed to assist the severely disabled patients to live independently. The system mainly consists of a non-invasive brain-machine interface (BMI) subsystem, a robot manipulator and a visual detection and localization subsystem. Different from most of the existing systems remotely controlled by joystick, head- or eye tracking, the proposed ID-SIR system directly acquires the intention from users' brain. Compared with the state-of-art system only working for a specific object in a fixed place, the designed ID-SIR system can grasp any desired object in a random place chosen by a user and deliver it to his/her mouth automatically...
2017: Frontiers in Neurorobotics
Omid Talakoub, Cesar Marquez-Chin, Milos R Popovic, Jessie Navarro, Erich T Fonoff, Clement Hamani, Willy Wong
In this study, we used electrocorticographic (ECoG) signals to extract the onset of arm movement as well as the velocity of the hand as a function of time. ECoG recordings were obtained from three individuals while they performed reaching tasks in the left, right and forward directions. The ECoG electrodes were placed over the motor cortex contralateral to the moving arm. Movement onset was detected from gamma activity with near perfect accuracy (> 98%), and a multiple linear regression model was used to predict the trajectory of the reaching task in three-dimensional space with an accuracy exceeding 85%...
2017: PloS One
Panagiotis Sapountzis, Georgia G Gregoriou
Understanding brain function and the computations that individual neurons and neuronal ensembles carry out during cognitive functions is one of the biggest challenges in neuroscientific research. To this end, invasive electrophysiological studies have provided important insights by recording the activity of single neurons in behaving animals. To average out noise, responses are typically averaged across repetitions and across neurons that are usually recorded on different days. However, the brain makes decisions on short time scales based on limited exposure to sensory stimulation by interpreting responses of populations of neurons on a moment to moment basis...
January 1, 2018: Frontiers in Bioscience (Landmark Edition)
Thanawin Trakoolwilaiwan, Bahareh Behboodi, Jaeseok Lee, Kyungsoo Kim, Ji-Woong Choi
The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI...
January 2018: Neurophotonics
Yin Tian, Huiling Zhang, Wei Xu, Haiyong Zhang, Li Yang, Shuxing Zheng, Yupan Shi
Spectral entropy, which was generated by applying the Shannon entropy concept to the power distribution of the Fourier-transformed electroencephalograph (EEG), was utilized to measure the uniformity of power spectral density underlying EEG when subjects performed the working memory tasks twice, i.e., before and after training. According to Signed Residual Time (SRT) scores based on response speed and accuracy trade-off, 20 subjects were divided into two groups, namely high-performance and low-performance groups, to undertake working memory (WM) tasks...
2017: Frontiers in Human Neuroscience
Martha G Garcia-Garcia, Austin J Bergquist, Hector Vargas-Perez, Mary K Nagai, Jose Zariffa, Cesar Marquez-Chin, Milos R Popovic
Context Firing rates of single cortical neurons can be volitionally modulated through biofeedback (i.e. operant conditioning), and this information can be transformed to control external devices (i.e. brain-machine interfaces; BMIs). However, not all neurons respond to operant conditioning in BMI implementation. Establishing criteria that predict neuron utility will assist translation of BMI research to clinical applications. Findings Single cortical neurons (n=7) were recorded extracellularly from primary motor cortex of a Long-Evans rat...
September 12, 2017: Journal of Spinal Cord Medicine
Kun Wang, Zhongpeng Wang, Yi Guo, Feng He, Hongzhi Qi, Minpeng Xu, Dong Ming
BACKGROUND: Motor imagery (MI) induced EEG patterns are widely used as control signals for brain-computer interfaces (BCIs). Kinetic and kinematic factors have been proved to be able to change EEG patterns during motor execution and motor imagery. However, to our knowledge, there is still no literature reporting an effective online MI-BCI using kinetic factor regulated EEG oscillations. This study proposed a novel MI-BCI paradigm in which users can online output multiple commands by imagining clenching their right hand with different force loads...
September 11, 2017: Journal of Neuroengineering and Rehabilitation
Rensong Liu, Zhiwen Zhang, Feng Duan, Xin Zhou, Zixuan Meng
Motor imagery (MI) electroencephalograph (EEG) signals are widely applied in brain-computer interface (BCI). However, classified MI states are limited, and their classification accuracy rates are low because of the characteristics of nonlinearity and nonstationarity. This study proposes a novel MI pattern recognition system that is based on complex algorithms for classifying MI EEG signals. In electrooculogram (EOG) artifact preprocessing, band-pass filtering is performed to obtain the frequency band of MI-related signals, and then, canonical correlation analysis (CCA) combined with wavelet threshold denoising (WTD) is used for EOG artifact preprocessing...
2017: Computational Intelligence and Neuroscience
Jennifer Chmura, Joshua Rosing, Steven Collazos, Shikha J Goodwin
Brain-computer interfaces (BCIs) are an emerging technology that are capable of turning brain electrical activity into commands for an external device. Motor imagery (MI)-when a person imagines a motion without executing it-is widely employed in BCI devices for motor control because of the endogenous origin of its neural control mechanisms, and the similarity in brain activation to actual movements. Challenges with translating a MI-BCI into a practical device used outside laboratories include the extensive training required, often due to poor user engagement and visual feedback response delays; poor user flexibility/freedom to time the execution/inhibition of their movements, and to control the movement type (right arm vs...
2017: Frontiers in Neurorobotics
Amy L Orsborn, Bijan Pesaran
Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning...
August 24, 2017: Current Opinion in Neurobiology
Marcos DelPozo-Banos, Carlos M Travieso, Jesus B Alonso, Ann John
Genetic and neurophysiological studies of electroencephalogram (EEG) have shown that an individual's brain activity during a given cognitive task is, to some extent, determined by their genes. In fact, the field of biometrics has successfully used this property to build systems capable of identifying users from their neural activity. These studies have always been carried out in isolated conditions, such as relaxing with eyes closed, identifying visual targets or solving mathematical operations. Here we show for the first time that the neural signature extracted from the spectral shape of the EEG is to a large extent independent of the recorded cognitive task and experimental condition...
July 3, 2017: International Journal of Neural Systems
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