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https://www.readbyqxmd.com/read/28924568/convolutional-neural-network-for-high-accuracy-functional-near-infrared-spectroscopy-in-a-brain-computer-interface-three-class-classification-of-rest-right-and-left-hand-motor-execution
#1
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
https://www.readbyqxmd.com/read/28912701/spectral-entropy-can-predict-changes-of-working-memory-performance-reduced-by-short-time-training-in-the-delayed-match-to-sample-task
#2
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
https://www.readbyqxmd.com/read/28899231/neuron-type-specific-utility-in-a-brain-machine-interface-a-pilot-study
#3
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
https://www.readbyqxmd.com/read/28893295/a-brain-computer-interface-driven-by-imagining-different-force-loads-on-a-single-hand-an-online-feasibility-study
#4
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
https://www.readbyqxmd.com/read/28874909/identification-of-anisomerous-motor-imagery-eeg-signals-based-on-complex-algorithms
#5
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
https://www.readbyqxmd.com/read/28860986/classification-of-movement-and-inhibition-using-a-hybrid-bci
#6
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
https://www.readbyqxmd.com/read/28843838/parsing-learning-in-networks-using-brain-machine-interfaces
#7
REVIEW
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
https://www.readbyqxmd.com/read/28835183/evidence-of-a-task-independent-neural-signature-in-the-spectral-shape-of-the-electroencephalogram
#8
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
https://www.readbyqxmd.com/read/28832013/model-and-experiments-to-optimize-co-adaptation-in-a-simplified-myoelectric-control-system
#9
Mathilde Couraud, Daniel Cattaert, Florent Paclet, Pierre-Yves Oudeyer, Aymar de Rugy
OBJECTIVE: To compensate for a limb lost in an amputation, myoelectric prostheses use surface electromyography (EMG) from the remaining muscles to control the prosthesis. Despite considerable progress, myoelectric controls remain markedly different from the way we normally control movements, and require intense user adaptation. To overcome this, our goal is to explore concurrent machine co-adaptation techniques that are developed in the field of brain-machine interface, and that are beginning to be used in myoelectric controls...
August 23, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28830308/online-eeg-classification-of-covert-speech-for-brain-computer-interfacing
#10
Alborz Rezazadeh Sereshkeh, Robert Trott, Aurélien Bricout, Tom Chau
Brain-computer interfaces (BCIs) for communication can be nonintuitive, often requiring the performance of hand motor imagery or some other conversation-irrelevant task. In this paper, electroencephalography (EEG) was used to develop two intuitive online BCIs based solely on covert speech. The goal of the first BCI was to differentiate between 10[Formula: see text]s of mental repetitions of the word "no" and an equivalent duration of unconstrained rest. The second BCI was designed to discern between 10[Formula: see text]s each of covert repetition of the words "yes" and "no"...
June 13, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28821649/effector-invariant-movement-encoding-in-the-human-motor-system
#11
Shlomi Haar, Ilan Dinstein, Ilan Shelef, Opher Donchin
Ipsilateral motor areas of cerebral cortex are active during arm movements and even reliably predict movement direction. Is coding similar during ipsilateral and contralateral movements? If so, is it in extrinsic (world-centered) or intrinsic (joint-configuration) coordinates? We addressed these questions by examining the similarity of multi-voxel fMRI patterns in visuomotor cortical regions during unilateral reaching movements with both arms. The results of three complementary analyses revealed that fMRI response patterns were similar across right and left arm movements to identical targets (extrinsic coordinates) in visual cortices, and across movements with equivalent joint-angles (intrinsic coordinates) in motor cortices...
August 16, 2017: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
https://www.readbyqxmd.com/read/28816702/as-above-so-below-towards-understanding-inverse-models-in-bci
#12
Jussi T Lindgren
In Brain-Computer Interfaces (BCI), measurements of the users brain activity are classified into commands for the computer. With EEG-based BCIs, the origins of the classified phenomena are often considered to be spatially localized in the cortical volume and mixed in the EEG. Does the reconstruction of the source activities in the volume help in building more accurate BCIs? The answer remains inconclusive despite previous work. In this paper, we study the question by contrasting the physiology-driven source reconstruction with data-driven representations obtained by statistical machine learning...
August 17, 2017: Journal of Neural Engineering
https://www.readbyqxmd.com/read/28813935/influence-of-artifacts-on-movement-intention-decoding-from-eeg-activity-in-severely-paralyzed-stroke-patients
#13
Eduardo Lopez-Larraz, Carlos Bibian, Niels Birbaumer, Ander Ramos-Murguialday
Brain-machine interfaces (BMI) can be used to control robotic and prosthetic devices for rehabilitation of motor disorders, such as stroke. The calibration of these BMI systems is of paramount importance in order to establish a precise contingent link between the brain activity related to movement intention and the peripheral feedback. However, electroencephalographic (EEG) activity, commonly used to build non-invasive BMIs, can be easily contaminated by artifacts of electrical or physiological origin. The way these interferences can affect the performance of movement intention decoders has not been deeply studied, especially when dealing with severely paralyzed patients, which often generate more artifacts by compensatory movements...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813934/a-hybrid-brain-machine-interface-based-on-eeg-and-emg-activity-for-the-motor-rehabilitation-of-stroke-patients
#14
Andrea Sarasola-Sanz, Nerea Irastorza-Landa, Eduardo Lopez-Larraz, Carlos Bibian, Florian Helmhold, Doris Broetz, Niels Birbaumer, Ander Ramos-Murguialday
Including supplementary information from the brain or other body parts in the control of brain-machine interfaces (BMIs) has been recently proposed and investigated. Such enriched interfaces are referred to as hybrid BMIs (hBMIs) and have been proven to be more robust and accurate than regular BMIs for assistive and rehabilitative applications. Electromyographic (EMG) activity is one of the most widely utilized biosignals in hBMIs, as it provides a quite direct measurement of the motion intention of the user...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813929/soft-brain-machine-interfaces-for-assistive-robotics-a-novel-control-approach
#15
Lucia Schiatti, Jacopo Tessadori, Giacinto Barresi, Leonardo S Mattos, Arash Ajoudani
Robotic systems offer the possibility of improving the life quality of people with severe motor disabilities, enhancing the individual's degree of independence and interaction with the external environment. In this direction, the operator's residual functions must be exploited for the control of the robot movements and the underlying dynamic interaction through intuitive and effective human-robot interfaces. Towards this end, this work aims at exploring the potential of a novel Soft Brain-Machine Interface (BMI), suitable for dynamic execution of remote manipulation tasks for a wide range of patients...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813811/a-multichannel-near-infrared-spectroscopy-triggered-robotic-hand-rehabilitation-system-for-stroke-patients
#16
Jongseung Lee, Nobutaka Mukae, Jumpei Arata, Hiroyuki Iwata, Keiji Iramina, Koji Iihara, Makoto Hashizume
There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom motion for a hand's closing and opening, is triggered by a wireless command from a NIRS system, capturing a subject's motor cortex activation...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28813805/improving-robotic-stroke-rehabilitation-by-incorporating-neural-intent-detection-preliminary-results-from-a-clinical-trial
#17
Jennifer L Sullivan, Nikunj A Bhagat, Nuray Yozbatiran, Ruta Paranjape, Colin G Losey, Robert G Grossman, Jose L Contreras-Vidal, Gerard E Francisco, Marcia K O'Malley
This paper presents the preliminary findings of a multi-year clinical study evaluating the effectiveness of adding a brain-machine interface (BMI) to the MAHI-Exo II, a robotic upper limb exoskeleton, for elbow flexion/extension rehabilitation in chronic stroke survivors. The BMI was used to trigger robot motion when movement intention was detected from subjects' neural signals, thus requiring that subjects be mentally engaged during robotic therapy. The first six subjects to complete the program have shown improvements in both Fugl-Meyer Upper-Extremity scores as well as in kinematic movement quality measures that relate to movement planning, coordination, and control...
July 2017: IEEE ... International Conference on Rehabilitation Robotics: [proceedings]
https://www.readbyqxmd.com/read/28769781/enhancing-classification-performance-of-functional-near-infrared-spectroscopy-brain-computer-interface-using-adaptive-estimation-of-general-linear-model-coefficients
#18
Nauman Khalid Qureshi, Noman Naseer, Farzan Majeed Noori, Hammad Nazeer, Rayyan Azam Khan, Sajid Saleem
In this paper, a novel methodology for enhanced classification of functional near-infrared spectroscopy (fNIRS) signals utilizable in a two-class [motor imagery (MI) and rest; mental rotation (MR) and rest] brain-computer interface (BCI) is presented. First, fNIRS signals corresponding to MI and MR are acquired from the motor and prefrontal cortex, respectively, afterward, filtered to remove physiological noises. Then, the signals are modeled using the general linear model, the coefficients of which are adaptively estimated using the least squares technique...
2017: Frontiers in Neurorobotics
https://www.readbyqxmd.com/read/28769776/affective-aspects-of-perceived-loss-of-control-and-potential-implications-for-brain-computer-interfaces
#19
Sebastian Grissmann, Thorsten O Zander, Josef Faller, Jonas Brönstrup, Augustin Kelava, Klaus Gramann, Peter Gerjets
Most brain-computer interfaces (BCIs) focus on detecting single aspects of user states (e.g., motor imagery) in the electroencephalogram (EEG) in order to use these aspects as control input for external systems. This communication can be effective, but unaccounted mental processes can interfere with signals used for classification and thereby introduce changes in the signal properties which could potentially impede BCI classification performance. To improve BCI performance, we propose deploying an approach that potentially allows to describe different mental states that could influence BCI performance...
2017: Frontiers in Human Neuroscience
https://www.readbyqxmd.com/read/28769747/movement-related-sensorimotor-high-gamma-activity-mainly-represents-somatosensory-feedback
#20
Seokyun Ryun, June S Kim, Eunjeong Jeon, Chun K Chung
Somatosensation plays pivotal roles in the everyday motor control of humans. During active movement, there exists a prominent high-gamma (HG >50 Hz) power increase in the primary somatosensory cortex (S1), and this provides an important feature in relation to the decoding of movement in a brain-machine interface (BMI). However, one concern of BMI researchers is the inflation of the decoding performance due to the activation of somatosensory feedback, which is not elicited in patients who have lost their sensorimotor function...
2017: Frontiers in Neuroscience
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