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Brain machine interfaces

Jane E Huggins, Christoph Guger, Mounia Ziat, Thorsten O Zander, Denise Taylor, Michael Tangermann, Aureli Soria-Frisch, John Simeral, Reinhold Scherer, Rüdiger Rupp, Giulio Ruffini, Douglas K R Robinson, Nick F Ramsey, Anton Nijholt, Gernot Müller-Putz, Dennis J McFarland, Donatella Mattia, Brent J Lance, Pieter-Jan Kindermans, Iñaki Iturrate, Christian Herff, Disha Gupta, An H Do, Jennifer L Collinger, Ricardo Chavarriaga, Steven M Chase, Martin G Bleichner, Aaron Batista, Charles W Anderson, Erik J Aarnoutse
The Sixth International Brain-Computer Interface (BCI) Meeting was held 30 May-3 June 2016 at the Asilomar Conference Grounds, Pacific Grove, California, USA. The conference included 28 workshops covering topics in BCI and brain-machine interface research. Topics included BCI for specific populations or applications, advancing BCI research through use of specific signals or technological advances, and translational and commercial issues to bring both implanted and non-invasive BCIs to market. BCI research is growing and expanding in the breadth of its applications, the depth of knowledge it can produce, and the practical benefit it can provide both for those with physical impairments and the general public...
2017: Brain Computer Interfaces
Nir Even-Chen, Sergey D Stavisky, Jonathan C Kao, Stephen I Ryu, Krishna V Shenoy
OBJECTIVE: Making mistakes is inevitable, but identifying them allows us to correct or adapt our behavior to improve future performance. Current brain-machine interfaces (BMIs) make errors that need to be explicitly corrected by the user, thereby consuming time and thus hindering performance. We hypothesized that neural correlates of the user perceiving the mistake could be used by the BMI to automatically correct errors. However, it was unknown whether intracortical outcome error signals were present in the premotor and primary motor cortices, brain regions successfully used for intracortical BMIs...
November 13, 2017: Journal of Neural Engineering
Benjamin Wittevrongel, Elia Van Wolputte, Marc M Van Hulle
When encoding visual targets using various lagged versions of a pseudorandom binary sequence of luminance changes, the EEG signal recorded over the viewer's occipital pole exhibits so-called code-modulated visual evoked potentials (cVEPs), the phase lags of which can be tied to these targets. The cVEP paradigm has enjoyed interest in the brain-computer interfacing (BCI) community for the reported high information transfer rates (ITR, in bits/min). In this study, we introduce a novel decoding algorithm based on spatiotemporal beamforming, and show that this algorithm is able to accurately identify the gazed target...
November 8, 2017: Scientific Reports
Tian-Ming Fu, Guosong Hong, Robert D Viveros, Tao Zhou, Charles M Lieber
Implantable electrical probes have led to advances in neuroscience, brain-machine interfaces, and treatment of neurological diseases, yet they remain limited in several key aspects. Ideally, an electrical probe should be capable of recording from large numbers of neurons across multiple local circuits and, importantly, allow stable tracking of the evolution of these neurons over the entire course of study. Silicon probes based on microfabrication can yield large-scale, high-density recording but face challenges of chronic gliosis and instability due to mechanical and structural mismatch with the brain...
November 6, 2017: Proceedings of the National Academy of Sciences of the United States of America
Yue Li, Shaomin Zhang, Yile Jin, Bangyu Cai, Marco Controzzi, Junming Zhu, Jianmin Zhang, Xiaoxiang Zheng
Electrocorticography (ECoG) has been demonstrated as a promising neural signal source for developing brain-machine interfaces (BMIs). However, many concerns about the disadvantages brought by large craniotomy for implanting the ECoG grid limit the clinical translation of ECoG-based BMIs. In this study, we collected clinical ECoG signals from the sensorimotor cortex of three epileptic participants when they performed hand gestures. The ECoG power spectrum in hybrid frequency bands was extracted to build a synchronous real-time BMI system...
2017: Behavioural Neurology
Shiu Kumar, Kabir Mamun, Alok Sharma
BACKGROUND: Classification of electroencephalography (EEG) signals for motor imagery based brain computer interface (MI-BCI) is an exigent task and common spatial pattern (CSP) has been extensively explored for this purpose. In this work, we focused on developing a new framework for classification of EEG signals for MI-BCI. METHOD: We propose a single band CSP framework for MI-BCI that utilizes the concept of tangent space mapping (TSM) in the manifold of covariance matrices...
October 24, 2017: Computers in Biology and Medicine
Stephanie Lees, Natalie Dayan, Hubert Cecotti, Paul McCullagh, Liam Maguire, Fabien Lotte, Damien H Coyle
Rapid serial visual presentation (RSVP) combined with the detection of event related brain responses facilitates the selection of relevant information contained in a stream of images presented rapidly to a human. Event related potentials (ERPs), measured non-invasively with electroencephalography (EEG), can be associated with infrequent target stimuli(images) in groups of images, potentially providing an interface for human-machine symbiosis, where humans can interact and interface with a computer without moving and which may offer faster image sorting than scenarios where humans are expected to physically react when a target image is detected...
November 3, 2017: Journal of Neural Engineering
Weifeng Liu, Xiaoming Liu, Ruomeng Dai, Xiaoying Tang
EEG-based motor imagery is very useful in brain-computer interface. How to identify the imaging movement is still being researched. Electroencephalography (EEG) microstates reflect the spatial configuration of quasi-stable electrical potential topographies. Different microstates represent different brain functions. In this paper, microstate method was used to process the EEG-based motor imagery to obtain microstate. The single-trial EEG microstate sequences differences between two motor imagery tasks - imagination of left and right hand movement were investigated...
November 3, 2017: Computer Assisted Surgery (Abingdon, England)
H Cecotti, A Barachant, J R King, J Sanchez Bornot, G Prasad
The recognition of brain evoked responses at the single-trial level is a challenging task. Typical non-invasive brain-computer interfaces based on event-related brain responses use eletroencephalograhy. In this study, we consider brain signals recorded with magnetoencephalography (MEG), and we expect to take advantage of the high spatial and temporal resolution for the detection of targets in a series of images. This study was used for the data analysis competition held in the 20th International Conference on Biomagnetism (Biomag) 2016, wherein the goal was to provide a method for single-trial detection of even-related fields corresponding to the presentation of happy faces during the rapid presentation of images of faces with six different facial expressions (anger, disgust, fear, neutrality, sadness, and happiness)...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Junjun Chen, Kai Xu, Zaiyue Yang, Yiwen Wang
Neuronal tuning property such as preferred direction and modulation depth could change gradually or abruptly in brain machine interface (BMI). The decoding performance will decay in static algorithms where dynamic neuronal tuning property is regarded as stationary. Many adaptive algorithms have been proposed to update the time-varying decoding parameter with main consideration on the decoding performance, but seldom focus on exploring how individual neuronal tuning property changes physiologically. We propose a novel adaptive algorithm based on sequential Monte Carlo point process estimation to capture the abrupt change of neuronal modulation depth and preferred direction...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Kha Vo, Diep N Nguyen, Ha Hoang Kha, Eryk Dutkiewicz
In most Brain-Computer Interface systems, especially the P300-Speller, there must be a harmonized balance between the accuracy and the spelling time. One major drawback of the classical 36-choice P300-Speller is the slow rate of character elicitation. This paper aims to propose a real-time signal processing method to decrease the spelling time by exploiting the score margins of the ensemble Support Vector Machine classifiers during real-time P300-Speller flashes, rather than just getting the classifiers' highest scores...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Kristofer E Bouchard, Alejandro F Bujan, Edward F Chang, Friedrich T Sommer
The concept of sparsity has proven useful to understanding elementary neural computations in sensory systems. However, the role of sparsity in motor regions is poorly understood. Here, we investigated the functional properties of sparse structure in neural activity collected with high-density electrocorticography (ECoG) from speech sensorimotor cortex (vSMC) in neurosurgical patients. Using independent components analysis (ICA), we found individual components corresponding to individual major oral articulators (i...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Kai Qian, Luiz Antonio Dos Anjos, Karthikeyan Balasubramanian, Kelsey Stilson, Carrie Balcer, Nicholas G Hatsopoulos, Derek G Kamper
While neurons in primary motor cortex (M1) have been shown to respond to sensory stimuli, exploration of this phenomenon has proven challenging. Accurate and repeatable presentation of sensory inputs is difficult. Here, we describe a novel paradigm to study response to joint motion and fingertip force. We employed a custom exoskeleton to drive index finger metacarpophalangeal joint (MCP) of a macaque to follow sinusoid trajectories at 4 different frequencies (0.2, 0.5, 1, 2Hz) and 2 movement ranges (68.4, 34...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Yaguang Jia, Jun Xie, Guanghua Xu, Min Li, Sicong Zhang, Ailing Luo, Xingliang Han
Signal processing is one of the key points in brain computer interface (BCI) application. The common methods in BCI signal classification include canonical correlation analysis (CCA), support vector machine (SVM) and so on. However, because BCI signals are very complex and valid signals often come with confounded background noise, many current classification methods would lose meaningful information embedded in human EEGs. Otherwise, due to the huge inter-subject variability with respect to characteristics and patterns of BCI signals, there often exists large difference of classification accuracy among different subjects...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Eduardo Lopez-Larraz, Andreas M Ray, Thiago C Figueiredo, Carlos Bibian, Niels Birbaumer, Ander Ramos-Murguialday
Recent studies have demonstrated the efficacy of brain-machine interfaces (BMI) for motor rehabilitation after stroke, especially for those patients with severe paralysis. However, a cerebro-vascular accident can affect the brain in many different manners, and lesions in diverse areas, even from significantly different volumes, can lead to similar or equal motor deficits. The location of the insult influences the way the brain activates when moving or attempting to move a paralyzed limb. Since the essence of a rehabilitative BMI is to precisely decode motor commands from the brain, it is crucial to characterize how lesion location affects the measured signals and if and how it influences BMI performance...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Hyeong-Jun Park, Jongin Kim, Beomjun Min, Boreom Lee
Performance of motor imagery based brain-computer interfaces (MI BCIs) greatly depends on how to extract the features. Various versions of filter-bank based common spatial pattern have been proposed and used in MI BCIs. Filter-bank based common spatial pattern has more number of features compared with original common spatial pattern. As the number of features increases, the MI BCIs using filter-bank based common spatial pattern can face overfitting problems. In this study, we used eigenvector centrality feature selection method, wavelet packet decomposition common spatial pattern, and kernel extreme learning machine to improve the performance of MI BCIs and avoid overfitting problems...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Shabnam Samima, Monalisa Sarma, Debasis Samanta
Vigilance or sustained attention is defined as the ability to maintain concentrated attention over prolonged time periods. It is an important aspect in industries such as aerospace and nuclear power, which involve tremendous man-machine interaction and where safety of any component/system or environment as a whole is extremely crucial. Many methods for vigilance detection, based on biological and behavioral characteristics, have been proposed in the literature. Nevertheless, the existing methods are associated with high time complexity, unhandy devices and incur huge equipment overhead...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
David Lee, Hee-Jae Lee, Sang-Hoon Park, Woo-Hyuk Jung, Jae-Ho Kim, Sang-Goog Lee
Traditional Support Vector Machine (SVM) is widely used classification method for brain-computer interface (BCI). However, SVM has a high computational complexity. In this paper, Gaussian Mixture Model (GMM)-based training data reduction is proposed to reduce high computational complexity. The proposed method is configured as follows: First, wavelet-based combined feature vectors are applied for motor imagery electroencephalography (EEG) identification and principal component analysis (PCA) are used to reduce the dimension of feature vectors...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Muhammad Naveed Iqbal Qureshi, Dongrae Cho, Boreom Lee
The accurate classification of the electroencephalography (EEG) signals is the most important task towards the development of a reliable motor imagery brain-computer interface (MI-BCI) system. In this study, we utilized a publically available BCI Competition-IV 2008 dataset IIa. This study address to the binary classification problem of the motor imagery EEG data by using a sigmoid activation function-based extreme learning machines (ELM). We proposed a novel method of extracting the features from the EEG signals by first applying the independent component analysis (ICA) on the time series data and transforming the ICA time series data into Fourier domain and then extract the phase information from the Fourier spectrum...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Tze Hui Koh, Camilo Libedinsky, Cuntai Guan, Kai Keng Ang, Rosa Q So
Invasive brain-machine-interface (BMI) has the prospect to empower tetraplegic patients with independent mobility through the use of brain-controlled wheelchairs. For the practical and long-term use of such control systems, the system has to distinguish between stop and movement states and has to be robust to overcome non-stationarity in the brain signals. In this work, we investigates the non-stationarity of the stop state on neural data collected from a macaque trained to control a robotic platform to stop and move in left, right, forward directions We then propose a hybrid approach that employs both random forest and linear discriminant analysis (LDA)...
July 2017: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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