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Drowsy driving is the main reason for sleep-related crashes. We have observed that an alpha wave attenuation-disappearance phenomenon and a typical alpha blocking phenomenon commonly exist in the eye closure events during daytime simulated driving experiments. These two alpha-related phenomena prove to respectively represent two different sleepiness levels: the sleep onset and the relaxed wakefulness...
Cross-channel maximum and minimum are used to monitor real-time electroencephalogram signals in 14 channels. On detection of a possible change, multivariate empirical mode decomposed the last 2 s of the signal into narrow-band intrinsic mode functions. Common spatial pattern is then utilised to create discriminating features for classification purpose. Logistic regression, artificial neural network,...
With the growing volume and complexity of air traffic, air traffic controllers (ATCOs) encounter heavier burden nowadays. Therefore, human factors study in air traffic control (ATC) is increasingly essential, paving the way to a safer air transportation system. In this paper, we conducted an ATC experiment, where Electroencephalogram (EEG) data were collected throughout the experiment. Compared to...
Virtual Reality (VR) research is accelerating the development of inexpensive real-time Brain Computer Interface (BCI). Hardware improvements that increase the capability of Virtual Reality displays and Brain Computer wearable sensors have made possible several new software frameworks for developers to use and create applications combining BCI and VR. It also enables multiple sensory pathways for communications...
Hybrid Brain-Computer Interfaces (BCI) has shown great promise for neuro-prosthetics and assistive devices in the field of rehabilitation. However, the complexity involved with the system design and time cost for classification of motor tasks is a core problem when we step into clinical applications. To help address this problem, simultaneous measurements of Electroencephalography (EEG) and functional...
In brain-computer interface (BCI) research, there must be a trade-off between accuracy and speed of the BCI system, especially those based on event-related potentials (ERPs). This paper proposes a novel method which can significantly increase the spelling bit rate while also maintaining the desired accuracy. We provide an adaptive real-time stopping method based on the scores of ensemble support vector...
As hybrid, passive brain-computer interface systems become more advanced, it is important to grow our understanding of how to produce generalizable pattern classifiers of physiological data. One of the most difficult problems in applying machine learning algorithms to these data types is nonstationarity, which can evolve over the course of hours and days, and is more susceptible to changes resulting...
This paper focuses on the real time control of autonomous robot through the detection of multiple types of motor imagery. The EEG data contains when subject performs imagery motor activity. The motor imagery activity includes movements of left hand, right hand, left leg and right leg. For feature extraction, the cross-correlation method is used to calculate six statistical features (mean, median,...
In this paper, we have investigated the feasibility of detecting drowsiness using hemodynamic brain signals for a passive brain-computer interface (BCI). Functional near-infrared spectroscopy (fNIRS) is used to measure the right dorsolateral-prefrontal brain region in order to investigate the hemodynamic changes corresponding to drowsy and alert states. The data is recorded using five drowsy subjects...
Stability of algorithms is very important for electroencephalogram (EEG) based applications. Stable features should exhibit consistency among repeated measurements of the same subject. Previously, power features were reported to be one of the most stable EEG features in medical application. In this paper, stability of features in emotion recognition algorithms is studied. Our hypothesis is that the...
Automatic sleep staging on an online basis has recently emerged as a research topic motivated by fundamental sleep research. The aim of this paper is to find optimal signal processing methods and machine learning algorithms to achieve online sleep staging on the basis of a single EEG signal. The classification performance obtained using six different EEG signals and various signal processing feature...
This paper is directed toward the development of a portable NIRS-BCI system, through investigation of different methods of brain activity classification in BCI. We first compare the performance of three classifiers (perceptron, BP network, and SVM) in classification of an ideal signal. We then investigate their performance in classification of actual data acquired in an experiment with volunteer participants...
We present the first step towards a brain computer interface (BCI) for communication using real-time functional magnetic resonance imaging (fMRI). The subject in the MR scanner sees a virtual keyboard and steers a cursor to select different letters that can be combined to create words. The cursor is moved to the left by activating the left hand, to the right by activating the right hand, down by activating...
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