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This work investigates the potential of a four-class motor-imagery-based brain-computer interface (BCI) using functional near-infrared spectroscopy (fNIRS). Four motor imagery tasks (right hand, left hand, right foot, and left foot tapping) were executed while motor cortex activity was recorded via fNIRS. Preliminary results from three participants suggest that this could be a viable BCI interface,...
In applying mental imagery brain-computer interfaces (BCIs) to end users, training is a key part for novice users to get control. In general learning situations, it is an established concept that a trainer assists a trainee to improve his/her aptitude in certain skills. In this work, we want to evaluate whether we can apply this concept in the context of event-related desynchronization (ERD) based,...
A system using electroencephalography (EEG) signals could enhance the detection of mental fatigue while driving a vehicle. This paper examines the classification between fatigue and alert states using an autoregressive (AR) model-based power spectral density (PSD) as the features extraction method and fuzzy particle swarm optimization with cross mutated of artificial neural network (FPSOCM-ANN) as...
In this study, a novel P300 based brain-computer interface (BCI) system using random set presentation pattern and employing the effect of face familiarity has been proposed and developed. While the effect of face familiarity is widely studied in the cognitive neurosciences, it has so far not been addressed for the purpose of BCI. We compare P300-based BCI performances of a conventional row-column...
Multimodal brain imaging data fusion is a scientifically interesting and clinically important topic; however, there is relatively little work on N-way data fusion. In this paper, we applied multi-set canonical correlation analysis (MCCA) to combine data of resting state fMRI, EEG and sMRI, in order to elucidate the abnormalities that underlie schizophrenia patients and also covary across multiple...
Previous electroencephalogram (EEG) studies have shown that cognitive workload can be estimated by using several types of cognitive tasks. In this study, we attempted to characterize cognitive tasks that have been used to manipulate workload for generating classification models. We carried out a comparative analysis between two representative types of working memory tasks: the n-back task and the...
To address the nonstationarity issue in EEG-based brain computer interface (BCI), the computational model trained using the training data needs to adapt to the data from the test sessions. In this paper, we propose a novel adaptation approach based on the divergence framework. Cross-session changes can be taken into consideration by searching the discriminative subspaces for test data on the manifold...
Successful brain-computer interfaces (BCIs) swiftly and accurately communicate the user's intention to a computer. Typically, information transfer rate (ITR) is used to measure the performance of a BCI. We propose a multi-step process to speed up detection and classification of the user's intent and maximize ITR. Users randomly looked at 4 frequency options on the interface in two sessions, one without...
We present an accurate seizure detection algorithm, and make a detailed comparison of two frequency analysis methods: a widely used stationary method — Fast Fourier Transform (FFT) and a relatively new nonstationary method — Hilbert-Huang Transform (HHT). Two public databases and one our own database were tested. The results show that our algorithm has very high accuracy compared with the state-of-the-art...
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have potential to provide a fast communication channel between human brain and external devices. In SSVEP-based BCIs, Canonical Correlation Analysis (CCA) has been widely used to detect frequency-coded SSVEPs due to its high efficiency and robustness. However, the detectability of SSVEPs differs among frequencies due...
We present an automated solution for the acquisition, processing and classification of electroencephalography (EEG) signals in order to remotely control a remotely located robotic hand executing communicative gestures. The Brain-Computer Interface (BCI) was implemented using the Steady State Visual Evoked Potential (SSVEP) approach, a low-latency and low-noise method for reading multiple non-time-locked...
The current trend to use Brain-Computer Interfaces (BCIs) with mobile devices mandates the development of efficient EEG data processing methods. In this paper, we demonstrate the performance of a Principal Component Analysis (PCA) ensemble classifier for P300-based spellers. We recorded EEG data from multiple subjects using the Emotiv neuroheadset in the context of a classical oddball P300 speller...
Human error often becomes a serious problem in dairy life. Recent studies have shown that failures of attention and motor errors can be captured before they actually occur in the alpha, theta, and beta-band powers of electroencephalograms (EEGs), suggesting the possibility that errors in motor responses can be predicted. The goal of this study was to use single-trial offline classification to examine...
The present study examined the neural markers measured in event-related potentials (ERPs) for immediate performance accuracy during a cognitive task with less conflict, i.e., a Stroop color-word matching task, in which participants were required to judge the congruency of two feature dimensions of a stimulus. In an effort to make ERP components more specific to distinct underlying neural substrates,...
A brain-computer interface (BCI) is a system for direct communication between brain and computer. The P300 BCI system relies on an oddball paradigm to elicit the P300. With the aim to extract different P300 feature information from different subjects and reduce the data amount of electroencephalogram (EEG) in P300 classification, a P300 feature extraction algorithm is proposed, which is based on independent...
AN electroencephalograph (EEG) based computer interface system, also known as brain-computer interface (BCI), offers a new means of computer interaction for those with paralysis or severe neuromuscular disorders. This paper illustrates a novel method using Self Organizing Feature Map (SOFM) to classify left-hand movement imagination, right-hand movement imagination, and word generation from EEG. Welch's...
Automation of Electroencephalogram (EEG) analysis can significantly help the neurologist during the diagnosis of epilepsy. During last few years lot of work has been done in the field of computer assisted analysis to detect an epileptic activity in an EEG. Still there is a significant amount of need to make these computer assisted EEG analysis systems more convenient and informative for a neurologist...
This paper investigates the spatial and temporal dynamics in multi-channel electrocardiographic (ECoG) time series signals using Coupled Hidden Markov Model (CHMM). The signals are recorded in a hand motion control task, when the subject uses a joystick to move a cursor appearing on the screen to hit a virtual target. We detect signal onset using two heuristic schemes based on the experiment process...
A hybrid principal component analysis (PCA)-based neural network with fuzzy membership function (NEWFM) is proposed for epileptic seizure detection. By combining PCA and NEWFM, the proposed method improves the accuracy in epileptic seizure detection. The PCA is used for wavelet feature enhancement needed to eliminate the sensitivity of noise, electrode artifacts, or redundancy. NEWFM, a model of neural...
This paper presents a new emotion recognition method which combines electroencephalograph (EEG) signals and pupillary response collected from eye tracker. We select 15 emotional film clips of 3 categories (positive, neutral and negative). The EEG signals and eye tracking data of five participants are recorded, simultaneously, while watching these videos. We extract emotion-relevant features from EEG...
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