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Adaptive learning is a promising approach to education, in which instructional methods and materials are selected according to the performance of students. In this manner, the learning process can be tailored to the needs and strengths of students in order to maximize efficiency. Advances in internet technology and portable devices has led to the development of e-learning platforms outside the traditional...
Long monitoring tasks without regular actions, are becoming increasingly common from aircraft pilots to train conductors as these systems grow more automated. These task contexts are challenging for the human operator because they require inputs at irregular and highly interspaced moments even though these actions are often critical. It has been shown that such conditions lead to divided and distracted...
In recent days, the classification of abnormal brain Electroencephalogram (EEG) signals is a demanding and challenging task. For this purpose, some of the classification techniques which include Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) are frequently used in the existing works. But, it has some drawbacks such as, the above mentioned techniques...
Wireless EEG monitoring systems have been successfully used for seizure detection outside clinical settings. The wireless EEG sensor nodes consume a considerable amount of battery energy to acquire, encode and transmit the data to the server side. In this paper, we introduce energy-efficient monitoring systems to increase the sensors' battery lifetime. Specifically, we propose a feature extraction...
Driver distraction is a significant cause of accidents leading to injuries and fatalities on the roadway. Driving is a complex task and demands continuous visual and cognitive attention on the primary task of driving. Drivers are at risk of responding more slowly or less suitable to intricate and dynamically changing situations that entail their complete attention.
This paper proposes a novel mobile healthcare system for remotely monitoring neuro-cognitive functions of impaired subjects and proposing possible treatments. Currently, only hospital centers perform similar analyses through fixed and wired electroencephalography (EEG) inspection. The solution proposed here works wirelessly and improves its accuracy learning by performances of the subject playing...
Error-related potentials (ErrP) have been increasingly studied in psychophysical experiments as well as for brain-machine interfacing. In the latter case, the generalisation capabilities of ErrP decoders is a crucial element to avoid frequent recalibration processes, thus increasing their usability. Previous studies have suggested that ErrP signals are rather stable across recording sessions. Also,...
Security and reconnaissance applications are prominent BCI paradigms which are less complex and sophisticated if there is no contamination in Electroencephalogram (EEG) signal. The better the quality of EEG signal ensures the better the performance (better Information Transfer Rate (ITR), high Signal to Noise Ratio (SNR), high Bandwidth (BW), and so on) of BCI paradigms. Drowsiness is one of the major...
A novel method for defining an index based on multi-level clustering of 40-Hz auditory steady state response is presented in this paper. The index is a measure of depth of anaesthesia which can help monitoring depth of anaesthesia more closely and accurately. Multi-level expectation maximization (EM) is used for clustering the recorded 40-Hz auditory steady state response signals recorded from human...
A key ability of the human brain is to monitor erroneous events and adjust behaviors accordingly. Electrophysiological and neuroimaging studies have demonstrated different brain activities related to errors. Meanwhile, the recognition of error-related brain activity as one aspect of performance monitoring has been reported for potential applications in clinical neuroscience and brain-machine interface,...
According to world health organization, stress is a significant problem of our times and affects both physical as well as the mental health of people. There are various traditional stress detection methods are available. Research in area of stress detection has developed many techniques for monitoring the human brain that can be used to study the human behavior. However, there are researches on stress...
This work presents our effort in analyzing human bio signals collected during sleep studies, to automatically detect events related to sleep disorders. We experiment with real sleep data collected using standard Polysomnography (PSG), and we detect events of interest from EEG signals, by segmenting the signal, extracting descriptive features from each segment, and applying supervised learning for...
Brain-machine interfaces (BMIs) have demonstrated how they can be used for reaching tasks with both invasive and non-invasive signal recording methods. Despite the constant improvements in this field, there still exist diverse factors to overcome before achieving a natural control. In particular, the high variability of the brain signals often leads to the incorrect decoding of the subject intentions,...
Vigilance analysis associated with safe driving based on EEG has drawn considerable attention of researchers in recent years. Preventing traffic accidents caused by low level vigilance is highly desirable. This paper presents a novel vigilance analysis system by evaluating electroencephalographic (EEG) changes. EEG signals are preprocessed with independent component analysis to eliminate noise from...
In this paper, the classification of epileptic and non-epileptic events from multi-channel EEG data is investigated using a large number of time and frequency domain features. In contrast to most of the evaluations found in the literature, in this paper the non-epileptic class consists of two types of paroxysmal episodes of loss of consciousness namely the psychogenic non epileptic seizure (PNES)...
This study demonstrates a feasible online user-state monitoring system using an affordable consumer-grade wireless brain wave headset. The system was able to track the workload level in real-time with approximately 78% accuracy.
CBER (content-based-EEG-retrieval) systems present short data segments as query samples for similar segments in EEG databases. These systems have many applications in large-scale data-mining, but require effective and verifiable retrieval strategies. This paper introduces a new feature strategy based on class probabilities calculated by LIBSVM classification using low-order autoregressive (AR) modeling...
Amplitude integrated electroencephalogram (aEEG), a cerebral function monitoring method, is widely used in response to the clinical needs for continuous EEG monitoring. The focus work of this paper is presenting a novel combined feature set of aEEG and applying random forest (RF) method to identify the normal and abnormal aEEG tracing. To that end, a complete experimental evaluation was conducted...
Epilepsy is a neurological disorder which affects the nervous system. Epileptic seizures are due to hyperactivity in certain parts of the brain. Automatic seizure detection helps in diagnosis and monitoring of epilepsy especially during long term recordings of EEG. This paper presents the bispectrum analysis of electroencephalogram (EEG) for the detection of epilepsy. Bispectrum is a higher order...
In this research, we focus on detecting stress based on electroencephalogram (EEG) method. An experiment has been conducted with 59 subjects, the results show that three EEG features from Fpz point, LZ-complexity, alpha relative power and the ratio of alpha power to beta power, are effective respectively in the stress detection using K-Nearest-Neighbor classifier, however Naive Bayesian classifier...
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