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Computer brain interface

Yuichi Nishiyama, Hidekazu Kanayama, Hiroshi Mori, Keiji Tada, Yasushi Yamamoto, Takashi Katsube, Haruo Takeshita, Kazunori Kawakami, Hajime Kitagaki
OBJECTIVES: This study examined the usefulness of statistical parametric mapping (SPM) for investigating postmortem changes on brain computed tomography (CT). METHODS: This retrospective study included 128 patients (23 - 100 years old) without cerebral abnormalities who underwent unenhanced brain CT before and after death. The antemortem CT (AMCT) scans and postmortem CT (PMCT) scans were spatially normalized using our original brain CT template, and postmortem changes of CT values (in Hounsfield units; HU) were analysed by the SPM technique...
October 21, 2016: European Radiology
Elias Abou Zeid, Alborz Rezazadeh Sereshkeh, Tom Chau
OBJECTIVE: In recent years, the readiness potential (RP), a type of pre-movement neural activity, has been investigated for asynchronous electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Since the RP is attenuated for involuntary movements, a BCI driven by RP alone could facilitate intentional control amid a plethora of unintentional movements. Previous studies have attempted single trial classification of RP via spatial and temporal filtering methods, or by combining the RP with event-related desynchronization...
October 20, 2016: Journal of Neural Engineering
E Kinney-Lang, B Auyeung, J Escudero
Rehabilitation applications using brain-computer interfaces (BCI) have recently shown encouraging results for motor recovery. Effective BCI neurorehabilitation has been shown to exploit neuroplastic properties of the brain through mental imagery tasks. However, these applications and results are currently restricted to adults. A systematic search reveals there is essentially no literature describing motor rehabilitative BCI applications that use electroencephalograms (EEG) in children, despite advances in such applications with adults...
October 20, 2016: Journal of Neural Engineering
Fang Zhang, WenMing Hong, Yan Guo, QianYing Guo, XiaoPeng Hu
BACKGROUND: Microsurgery is a common treatment of brainstem tumors. However, misdirection, vascular damage, nerves injuries, paralysis, even death are all well-known complications, and the risk of adverse events is more likely in less experienced operators. This study was aimed to validate the accuracy of multimodal neuronavigation during microsurgery resection of brainstem tumors. METHODS: Ten patients with brainstem tumors underwent preoperative MRI, diffusion tensor imaging, computed tomography, three-dimensional print, and images loaded into the neuronavigation platform were used for its segmentation and preoperative planning...
October 14, 2016: Journal of Craniofacial Surgery
Kejia Hu, Chao Chen, Qingyao Meng, Ziv Williams, Wendong Xu
BACKGROUND: With the tremendous advances in the field of brain-computer interfaces (BCI), the literature in this field has grown exponentially; examination of highly cited articles is a tool that can help identify outstanding scientific studies and landmark papers. This study examined the characteristics of 100 highly cited BCI papers over the past 10 years. METHODS: The Web of Science was searched for highly cited papers related to BCI research published from 2006 to 2015...
October 14, 2016: Neuroscience Letters
Akshansh Gupta, Dhirendra Kumar
A brain computer interface (BCI) is a communication system by which a person can send messages or requests for basic necessities without using peripheral nerves and muscles. Response to mental task-based BCI is one of the privileged areas of investigation. Electroencephalography (EEG) signals are used to represent the brain activities in the BCI domain. For any mental task classification model, the performance of the learning model depends on the extraction of features from EEG signal. In literature, wavelet transform and empirical mode decomposition are two popular feature extraction methods used to analyze a signal having non-linear and non-stationary property...
September 3, 2016: Brain Informatics
Sebastian Halder, Kouji Takano, Hiroki Ora, Akinari Onishi, Kota Utsumi, Kenji Kansaku
Gaze-independent brain-computer interfaces (BCIs) are a possible communication channel for persons with paralysis. We investigated if it is possible to use auditory stimuli to create a BCI for the Japanese Hiragana syllabary, which has 46 Hiragana characters. Additionally, we investigated if training has an effect on accuracy despite the high amount of different stimuli involved. Able-bodied participants (N = 6) were asked to select 25 syllables (out of fifty possible choices) using a two step procedure: First the consonant (ten choices) and then the vowel (five choices)...
2016: Frontiers in Neuroscience
Andreas Pinegger, Selina C Wriessnegger, Josef Faller, Gernot R Müller-Putz
One important aspect in non-invasive brain-computer interface (BCI) research is to acquire the electroencephalogram (EEG) in a proper way. From an end-user perspective, it means with maximum comfort and without any extra inconveniences (e.g., washing the hair), whereas from a technical perspective, the signal quality has to be optimal to make the BCI work effectively and efficiently. In this work, we evaluated three different commercially available EEG acquisition systems that differ in the type of electrodes (gel-, water-, and dry-based), the amplifier technique, and the data transmission method...
2016: Frontiers in Neuroscience
Johanna Metsomaa, Jukka Sarvas, Risto J Ilmoniemi
OBJECTIVE: Blind source separation (BSS) can be used to decompose complex electroencephalography (EEG) or magnetoencephalography data into simpler components based on statistical assumptions without using a physical model. Applications include brain-computer interfaces, artifact removal and identifying parallel neural processes. We wish to address the issue of applying BSS to event-related responses which is challenging because of non-stationary data. METHODS: We introduce a new BSS approach called momentary-uncorrelated component analysis (MUCA) which is tailored for event-related multi-trial data...
October 12, 2016: IEEE Transactions on Bio-medical Engineering
Bethel C A Osuagwu, Leslie Wallace, Mathew Fraser, Aleksandra Vuckovic
OBJECTIVE: To compare neurological and functional outcomes between two groups of hospitalised patients with subacute tetraplegia. APPROACH: Seven patients received 20 sessions of brain computer interface (BCI) controlled functional electrical stimulation (FES) while five patients received the same number of sessions of passive FES for both hands. The neurological assessment measures were event related desynchronization (ERD) during movement attempt, Somatosensory evoked potential (SSEP) of the ulnar and median nerve; assessment of hand function involved the range of motion (ROM) of wrist and manual muscle test...
October 14, 2016: Journal of Neural Engineering
Alexey Petrushin, Lorenzo Ferrara, Axel Blau
OBJECTIVE: In light of recent progress in mapping neural function to behavior, we briefly and selectively review past and present endeavors to reveal and reconstruct nervous system function in Caenorhabditis elegans through simulation. APPROACH: Rather than presenting an all-encompassing review on the mathematical modeling of C. elegans, this contribution collects snapshots of pathfinding key works and emerging technologies that recent single- and multi-center simulation initiatives are building on...
October 14, 2016: Journal of Neural Engineering
Á Fernández-Rodríguez, F Velasco-Álvarez, R Ron-Angevin
This paper presents a review of the state of the art regarding wheelchairs driven by a brain-computer interface. Using a brain-controlled wheelchair (BCW), disabled users could handle a wheelchair through their brain activity, granting autonomy to move through an experimental environment. A classification is established, based on the characteristics of the BCW, such as the type of electroencephalographic signal used, the navigation system employed by the wheelchair, the task for the participants, or the metrics used to evaluate the performance...
October 14, 2016: Journal of Neural Engineering
Wolfgang Wiedemair, Zeljko Tukovic, Hrvoje Jasak, Dimos Poulikakos, Vartan Kurtcuoglu
Encapsulated microbubbles (MBs) serve as endovascular agents in a wide range of medical ultrasound applications. The oscillatory response of these agents to ultrasonic excitation is determined by MB size, gas content, viscoelastic shell properties and geometrical constraints. The viscoelastic parameters of the MB capsule vary during an oscillation cycle and change irreversibly upon shell rupture. The latter results in marked stress changes on the endothelium of capillary blood vessels due to altered MB dynamics...
October 12, 2016: Biomechanics and Modeling in Mechanobiology
Noman Naseer, Nauman Khalid Qureshi, Farzan Majeed Noori, Keum-Shik Hong
We analyse and compare the classification accuracies of six different classifiers for a two-class mental task (mental arithmetic and rest) using functional near-infrared spectroscopy (fNIRS) signals. The signals of the mental arithmetic and rest tasks from the prefrontal cortex region of the brain for seven healthy subjects were acquired using a multichannel continuous-wave imaging system. After removal of the physiological noises, six features were extracted from the oxygenated hemoglobin (HbO) signals. Two- and three-dimensional combinations of those features were used for classification of mental tasks...
2016: Computational Intelligence and Neuroscience
Nicholas R Waytowich, Vernon J Lawhern, Addison W Bohannon, Kenneth R Ball, Brent J Lance
Recent advances in signal processing and machine learning techniques have enabled the application of Brain-Computer Interface (BCI) technologies to fields such as medicine, industry, and recreation; however, BCIs still suffer from the requirement of frequent calibration sessions due to the intra- and inter-individual variability of brain-signals, which makes calibration suppression through transfer learning an area of increasing interest for the development of practical BCI systems. In this paper, we present an unsupervised transfer method (spectral transfer using information geometry, STIG), which ranks and combines unlabeled predictions from an ensemble of information geometry classifiers built on data from individual training subjects...
2016: Frontiers in Neuroscience
P Gonzalez-Navarro, M Moghadamfalahi, M Akcakaya, D Erdogmus
Multichannel electroencephalography (EEG) is widely used in non-invasive brain computer interfaces (BCIs) for user intent inference. EEG can be assumed to be a Gaussian process with unknown mean and autocovariance, and the estimation of parameters is required for BCI inference. However, the relatively high dimensionality of the EEG feature vectors with respect to the number of labeled observations lead to rank deficient covariance matrix estimates. In this manuscript, to overcome ill-conditioned covariance estimation, we propose a structure for the covariance matrices of the multichannel EEG signals...
February 2017: Signal Processing
Irene Rembado, Elisa Castagnola, Luca Turella, Tamara Ius, Riccardo Budai, Alberto Ansaldo, Gian Nicola Angotzi, Francesco Debertoldi, Davide Ricci, Miran Skrap, Luciano Fadiga
High-density surface microelectrodes for electrocorticography (ECoG) have become more common in recent years for recording electrical signals from the cortex. With an acceptable invasiveness/signal fidelity trade-off and high spatial resolution, micro-ECoG is a promising tool to resolve fine task-related spatial-temporal dynamics. However, volume conduction - not a negligible phenomenon - is likely to frustrate efforts to obtain reliable and resolved signals from a sub-millimeter electrode array. To address this issue, we performed an independent component analysis (ICA) on micro-ECoG recordings of somatosensory-evoked potentials (SEPs) elicited by median nerve stimulation in three human patients undergoing brain surgery for tumor resection...
August 18, 2016: International Journal of Neural Systems
Sammy Krachunov, Alexander J Casson
Electroencephalography (EEG) is a procedure that records brain activity in a non-invasive manner. The cost and size of EEG devices has decreased in recent years, facilitating a growing interest in wearable EEG that can be used out-of-the-lab for a wide range of applications, from epilepsy diagnosis, to stroke rehabilitation, to Brain-Computer Interfaces (BCI). A major obstacle for these emerging applications is the wet electrodes, which are used as part of the EEG setup. These electrodes are attached to the human scalp using a conductive gel, which can be uncomfortable to the subject, causes skin irritation, and some gels have poor long-term stability...
October 2, 2016: Sensors
Alianna J Maren
Effective Brain-Computer Interfaces (BCIs) require that the time-varying activation patterns of 2-D neural ensembles be modelled. The cluster variation method (CVM) offers a means for the characterization of 2-D local pattern distributions. This paper provides neuroscientists and BCI researchers with a CVM tutorial that will help them to understand how the CVM statistical thermodynamics formulation can model 2-D pattern distributions expressing structural and functional dynamics in the brain. The premise is that local-in-time free energy minimization works alongside neural connectivity adaptation, supporting the development and stabilization of consistent stimulus-specific responsive activation patterns...
September 30, 2016: Brain Sciences
B O Mainsah, L M Collins, C S Throckmorton
OBJECTIVE: The P300 speller is a popular brain-computer interface (BCI) system that has been investigated as a potential communication alternative for individuals with severe neuromuscular limitations. To achieve acceptable accuracy levels for communication, the system requires repeated data measurements in a given signal condition to enhance the signal-to-noise ratio of elicited brain responses. These elicited brain responses, which are used as control signals, are embedded in noisy electroencephalography (EEG) data...
October 5, 2016: Journal of Neural Engineering
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