Read by QxMD icon Read


Eva-Maria Kurz, Guilherme Wood, Silvia Erika Kober, Walter Schippinger, Gerald Pichler, Gernot Müller-Putz, Günther Bauernfeind
BACKGROUND: Recently, fNIRS has been proposed as a promising approach for awareness detection, and a possible method to establish basic communication in patients with disorders of consciousness (DOC). AIM: Using fNIRS, the present study evaluated the applicability of auditory presented mental-arithmetic tasks in this respect. METHODS: We investigated the applicability of active attention to serial subtractions for awareness detection in ten healthy controls (HC, 21-32 y/o), by comparing the measured patterns to patterns induced by self-performance of the same task...
June 14, 2018: Brain and Cognition
Cagatay Murat Yilmaz, Cemal Kose, Bahar Hatipoglu
BACKGROUND AND OBJECTIVE: Electroencephalography (EEG) is a method that measures and records the electrical activity of the human brain. These biomedical signals are currently being actively used in many research fields and have a wide range of potential uses in brain-computer interfaces (BCIs). The main aim of the present work is to improve the classification of EEG patterns for EEG-based BCI systems. METHODS: In this paper, we presented a classification approach for EEG-based BCIs...
August 2018: Computer Methods and Programs in Biomedicine
Rosaleena Mohanty, Anita M Sinha, Alexander B Remsik, Keith C Dodd, Brittany M Young, Tyler Jacobson, Matthew McMillan, Jaclyn Thoma, Hemali Advani, Veena A Nair, Theresa J Kang, Kristin Caldera, Dorothy F Edwards, Justin C Williams, Vivek Prabhakaran
Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well-understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages...
2018: Frontiers in Neuroscience
Ahmadreza Keihani, Zahra Shirzhiyan, Morteza Farahi, Elham Shamsi, Amin Mahnam, Bahador Makkiabadi, Mohsen R Haidari, Amir H Jafari
Background: Recent EEG-SSVEP signal based BCI studies have used high frequency square pulse visual stimuli to reduce subjective fatigue. However, the effect of total harmonic distortion (THD) has not been considered. Compared to CRT and LCD monitors, LED screen displays high-frequency wave with better refresh rate. In this study, we present high frequency sine wave simple and rhythmic patterns with low THD rate by LED to analyze SSVEP responses and evaluate subjective fatigue in normal subjects. Materials and Methods: We used patterns of 3-sequence high-frequency sine waves (25, 30, and 35 Hz) to design our visual stimuli...
2018: Frontiers in Human Neuroscience
Angela Riccio, Francesca Schettini, Luca Simione, Alessia Pizzimenti, Maurizio Inghilleri, Marta Olivetti-Belardinelli, Donatella Mattia, Febo Cincotti
Our objective was to investigate the capacity to control a P3-based brain-computer interface (BCI) device for communication and its related (temporal) attention processing in a sample of amyotrophic lateral sclerosis (ALS) patients with respect to healthy subjects. The ultimate goal was to corroborate the role of cognitive mechanisms in event-related potential (ERP)-based BCI control in ALS patients. Furthermore, the possible differences in such attentional mechanisms between the two groups were investigated in order to unveil possible alterations associated with the ALS condition...
2018: Frontiers in Human Neuroscience
S Buczinski, G Fecteau, J Dubuc, D Francoz
Bovine respiratory disease complex is a major cause of illness in dairy calves. The diagnosis of active infection of the lower respiratory tract is challenging on daily basis in the absence of accurate clinical signs. Clinical scoring systems such as the Californian scoring system, are appealing but were developed without considering the imperfection of reference standard tests used for case definition. This study used a Bayesian latent class model to update Californian prediction rules. The results of clinical examination and ultrasound findings of 608 preweaned dairy calves were used...
August 1, 2018: Preventive Veterinary Medicine
G R Kiran Kumar, M Ramasubba Reddy
BACKGROUND: Traditional Spatial filters used for steady-state visual evoked potential (SSVEP) extraction such as minimum energy combination (MEC) require the estimation of the background electroencephalogram (EEG) noise components. Even though this leads to improved performance in low signal to noise ratio (SNR) conditions, it makes such algorithms slow compared to the standard detection methods like canonical correlation analysis (CCA) due to the additional computational cost. NEW METHOD: In this paper, Periodic component analysis (πCA) is presented as an alternative spatial filtering approach to extract the SSVEP component effectively without involving extensive modelling of the noise...
June 8, 2018: Journal of Neuroscience Methods
Nasir Rashid, Javaid Iqbal, Amna Javed, Mohsin I Tiwana, Umar Shahbaz Khan
Brain Computer Interface (BCI) determines the intent of the user from a variety of electrophysiological signals. These signals, Slow Cortical Potentials, are recorded from scalp, and cortical neuronal activity is recorded by implanted electrodes. This paper is focused on design of an embedded system that is used to control the finger movements of an upper limb prosthesis using Electroencephalogram (EEG) signals. This is a follow-up of our previous research which explored the best method to classify three movements of fingers (thumb movement, index finger movement, and first movement)...
2018: BioMed Research International
Ian E R Waudby-Smith, Nam Tran, Joel A Dubin, Joon Lee
BACKGROUND: Nursing notes have not been widely used in prediction models for clinical outcomes, despite containing rich information. Advances in natural language processing have made it possible to extract information from large scale unstructured data like nursing notes. This study extracted the sentiment-impressions and attitudes-of nurses, and examined how sentiment relates to 30-day mortality and survival. METHODS: This study applied a sentiment analysis algorithm to nursing notes extracted from MIMIC-III, a public intensive care unit (ICU) database...
2018: PloS One
Qingguo Wei, Yonghui Liu, Xiaorong Gao, Yijun Wang, Chen Yang, Zongwu Lu, Huayuan Gong
In an existing brain-computer interface (BCI) based on code modulated visual evoked potentials (c-VEP), a method with which to increase the number of targets without increasing code length has not yet been established. In this paper, a novel c-VEP BCI paradigm, namely, grouping modulation with different codes that have good autocorrelation and crosscorrelation properties, is presented to increase the number of targets and information transfer rate (ITR). All stimulus targets are divided into several groups and each group of targets are modulated by a distinct pseudorandom binary code and its circularly shifting codes...
June 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Jun Xiao, Qiuyou Xie, Qing Lin, Tianyou Yu, Ronghao Yu, Yuanqing Li
Visual pursuit assessment is extensively applied in the behavioral scale-based clinical examination of patients with disorders of consciousness (DOC). However, this assessment is challenging because it relies on behavioral markers, and these patients severely lack behavioral responses. Brain-computer interfaces (BCIs) may provide a potential solution to detect brain responses to external stimuli without requiring behavioral expressions. A BCI system was designed to simulate visual pursuit detection in the coma recovery scale-revised (CRS-R)...
June 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Jaeyoung Shin, Do-Won Kim, Klaus-Robert Müller, Han-Jeong Hwang
Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are non-invasive neuroimaging methods that record the electrical and metabolic activity of the brain, respectively. Hybrid EEG-NIRS brain-computer interfaces (hBCIs) that use complementary EEG and NIRS information to enhance BCI performance have recently emerged to overcome the limitations of existing unimodal BCIs, such as vulnerability to motion artifacts for EEG-BCI or low temporal resolution for NIRS-BCI. However, with respect to NIRS-BCI, in order to fully induce a task-related brain activation, a relatively long trial length (≥10 s) is selected owing to the inherent hemodynamic delay that lowers the information transfer rate (ITR; bits/min)...
June 5, 2018: Sensors
Rosanne Zerafa, Tracey Camilleri, Owen Falzon, Kenneth P Camilleri
OBJECTIVE: Despite the vast research aimed at improving the performance of steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs), several limitations exist that restrict the use of such applications for long-term users in the real-world. One of the main challenges has been to reduce user training while maintaining good BCI performance. Motivated by the same aim, this survey identifies and compares the different training requirements of feature extraction methods for SSVEP-based BCIs...
June 5, 2018: Journal of Neural Engineering
Stavros I Dimitriadis, Avraam D Marimpis
A brain-computer interface (BCI) is a channel of communication that transforms brain activity into specific commands for manipulating a personal computer or other home or electrical devices. In other words, a BCI is an alternative way of interacting with the environment by using brain activity instead of muscles and nerves. For that reason, BCI systems are of high clinical value for targeted populations suffering from neurological disorders. In this paper, we present a new processing approach in three publicly available BCI data sets: (a) a well-known multi-class ( N = 6) coded-modulated Visual Evoked potential (c-VEP)-based BCI system for able-bodied and disabled subjects; (b) a multi-class ( N = 32) c-VEP with slow and fast stimulus representation; and (c) a steady-state Visual Evoked potential (SSVEP) multi-class ( N = 5) flickering BCI system...
2018: Frontiers in Neuroinformatics
Jiahui Pan, Qiuyou Xie, Haiyun Huang, Yanbin He, Yuping Sun, Ronghao Yu, Yuanqing Li
For patients with disorders of consciousness (DOC), such as vegetative state (VS) and minimally conscious state (MCS), detecting and assessing the residual cognitive functions of the brain remain challenging. Emotion-related cognitive functions are difficult to detect in patients with DOC using motor response-based clinical assessment scales such as the Coma Recovery Scale-Revised (CRS-R) because DOC patients have motor impairments and are unable to provide sufficient motor responses for emotion-related communication...
2018: Frontiers in Human Neuroscience
Thibault Gateau, Hasan Ayaz, Frédéric Dehais
There is growing interest for implementing tools to monitor cognitive performance in naturalistic work and everyday life settings. The emerging field of research, known as neuroergonomics, promotes the use of wearable and portable brain monitoring sensors such as functional near infrared spectroscopy (fNIRS) to investigate cortical activity in a variety of human tasks out of the laboratory. The objective of this study was to implement an on-line passive fNIRS-based brain computer interface to discriminate two levels of working memory load during highly ecological aircraft piloting tasks...
2018: Frontiers in Human Neuroscience
Josep Dinarès-Ferran, Rupert Ortner, Christoph Guger, Jordi Solé-Casals
EEG-based Brain-Computer Interfaces (BCIs) are becoming a new tool for neurorehabilitation. BCIs are used to help stroke patients to improve the functional capability of the impaired limbs, and to communicate and assess the level of consciousness in Disorder of Consciousness (DoC) patients. BCIs based on a motor imagery paradigm typically require a training period to adapt the system to each user's brain, and the BCI then creates and uses a classifier created with the acquired EEG. The quality of this classifier relies on amount of data used for training...
2018: Frontiers in Neuroscience
Eva M Hammer, Sebastian Halder, Sonja C Kleih, Andrea Kübler
Brain-Computer Interfaces (BCIs) provide communication channels independent from muscular control. In the current study we used two versions of the P300-BCI: one based on visual the other on auditory stimulation. Up to now, data on the impact of psychological variables on P300-BCI control are scarce. Hence, our goal was to identify new predictors with a comprehensive psychological test-battery. A total of N = 40 healthy BCI novices took part in a visual and an auditory BCI session. Psychological variables were measured with an electronic test-battery including clinical, personality, and performance tests...
2018: Frontiers in Neuroscience
Yuwei Zhao, Jiuqi Han, Yushu Chen, Hongji Sun, Jiayun Chen, Ang Ke, Yao Han, Peng Zhang, Yi Zhang, Jin Zhou, Changyong Wang
Multichannel electroencephalography (EEG) is widely used in typical brain-computer interface (BCI) systems. In general, a number of parameters are essential for a EEG classification algorithm due to redundant features involved in EEG signals. However, the generalization of the EEG method is often adversely affected by the model complexity, considerably coherent with its number of undetermined parameters, further leading to heavy overfitting. To decrease the complexity and improve the generalization of EEG method, we present a novel l 1 -norm-based approach to combine the decision value obtained from each EEG channel directly...
2018: Frontiers in Neuroscience
Fuwang Wang, Xiaolei Zhang, Rongrong Fu, Guangbin Sun
A home-auxiliary robot platform is developed in the current study which could assist patients with physical disabilities and older persons with mobility impairments. The robot, mainly controlled by brain computer interface (BCI) technology, can not only perform actions in a person's field of vision, but also work outside the field of vision. The wavelet decomposition (WD) is used in this study to extract the δ (0~4 Hz) and θ (4~8 Hz) sub-bands of subjects' electroencephalogram (EEG) signals. The correlation between pairs of 14 EEG channels is determined with synchronization likelihood (SL), and the brain network structure is generated...
June 1, 2018: Sensors
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"