Selection of valid and reliable EEG features for predicting auditory and visual alertness levels (original) (raw)
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In this paper, we focus on identifying the alertness state of subjects undergoing the cortical auditory evoked potential (CAEP) hearing test. A supervised classification approach is adopted, where subjects were advised to indicate their alertness states in specified time instances. Two sets of features are considered here to represent the recorded data. The first is based on the wavelet transform of the background EEG, while the second is obtained from the peaks of the CAEP responses. The rational behind using the second feature set is to evaluate the relationship between CAEP responses and alertness levels. Obtained results suggest that the CAEP-based features are very comparable, in terms of classification accuracy, to the well-established wavelet-based features of EEG signals (79% compared to 80%). The findings of this paper will contribute towards a better understanding of CAEP responses at the different alertness states.
Assessing alertness from EEG power spectral bands
The assessment of low level of alertness and drowsiness conditions of humans, while performing critical task, requires the development of automatic detection systems to work in real time, to be as pervasive as possible for long lasting periods of use and robust enough to cope whit a wide intra- and inter-individual variability. A new alertness detection procedure based on the spectral analysis of the EEG signal is proposed, mostly concerned with the provision of robust classification criteria under the working conditions depicted above. The wide inter-individual variability has been reduced down to operational levels by means of a personal dependant normalization algorithm, which consists of describing the EEG spectral morphology as a fuction of the alpha behaviour of each subject. With this approach, drownsiness classification can be achieved by simple thresholding of the EEG spectral variable selected: the power ratio between a high frequency and an alpha bands defined for each in...
In this paper, we analyze the EEG rhythms of subjects undergoing the cortical auditory evoked potential (CAEP) hearing test. Investigation of the importance of the different EEG rhythms in terms of their capability in differentiating between the different alertness states when considering 64 channel EEG montage is conducted. This is followed by considering subsets that contain 2, 3, 4 as well as all 5 EEG rhythms. Finally, a feature subset selection method based on differential evolution (DE) that has particularly been proposed to deal with multi-channel signals is used to search for the best subset of EEG rhythms for the various channels.
Which EEG Electrodes Should Be Considered for Alertness Assessment?
2019
The analysis of EEG signal is one of the objective methods used in alertness assessment. Many publications confirm the correct assessment of alertness level based on the analysis of selected brain waves. EEG registration is a difficult task; one of the important problems is the necessity to choose which EEG electrode to download the signal for analysis. The authors use different electrodes, often without justifying the choice. Equally often, the only justification is to say that the analyzed signal was the strongest among those available, or the least contaminated with artifacts. The aim of the article is to try to answer the question: signals which electrodes (channels) should be included in the alertness assessment. 33 participants took part in the experiment. Blue and red light was used to stimulate alertness. The impact of such light is documented in many publications. Alertness changes due to specific color of light were evaluated-the changes of alpha and beta bands were analyzed. Statistical analysis has shown that for alertness assessment the following electrodes should be considered: C3 and FC1 for alpha band and F3 and FP1-for beta band signals.
Biological Psychology, 2011
A great deal of research over the last century has focused on drowsiness/alertness detection, as fatigue-related physical and cognitive impairments pose a serious risk to public health and safety. Available drowsiness/alertness detection solutions are unsatisfactory for a number of reasons: 1) lack of generalizability, 2) failure to address individual variability in generalized models, and/or 3) they lack a portable, un-tethered application. The current study aimed to address these issues, and determine if an individualized electroencephalography (EEG) based algorithm could be defined to track performance decrements associated with sleep loss, as this is the first step in developing a field deployable drowsiness/alertness detection system. The results indicated that an EEG-based algorithm, individualized using a series of brief "identification" tasks, was able to effectively track performance decrements associated with sleep deprivation. Future development will address the need for the algorithm to predict performance decrements due to sleep loss, and provide field applicability.
Application of Classical and Model-Based Spectral Methods to Describe the State of Alertness in EEG
Electrophysiological recordings are considered a reliable method of assessing a person's alertness. Sleep medicine is asked to offer objective methods to measure daytime alertness, tiredness and sleepiness. In this study, EEG signals recorded from 30 subjects were processed by PC-computer using classical and model-based methods. The classical method (fast Fourier transform) and three model-based methods (Burg autoregresse, moving average, least-squares modified Yule-Walker autoregressive moving average methods) were selected for processing EEG signals to discriminate the alertness level of subject. Power spectra of EEG signals were obtained by using these spectrum analysis techniques. These EEG spectra were then used to compare the applied methods in terms of their frequency resolution and the effects in determination of vigilance state of subject. It is found that, FFT and MA methods have low spectral resolution, these two methods are not appropriate for the analysis of the a wake-sleep correlation. Burg AR and least-squares modified Yule-Walker ARMA methods' performance characteristics have been found extremely valuable for the determination of vigilance state of a healthy subject, because of their clear spectra.
Estimating alertness from the EEG power spectrum
1997
In tasks requiring sustained attention, human alertness varies on a minute time scale. This can have serious consequences in occupations ranging from air tra c control to monitoring of nuclear power plants. Changes in the electroencephalographic (EEG) power spectrum accompany these uctuations in the level of alertness, as assessed by measuring simultaneous changes in EEG and performance on an auditory monitoring task. By combining power spectrum estimation, principal component analysis and arti cial neural networks, we show that continuous, accurate, noninvasive, and near real-time estimation of an operator's global level of alertness is feasible using EEG measures recorded from as few as two central scalp sites. This demonstration could lead to a practical system for noninvasive monitoring of the cognitive state of human operators in attention-critical settings.
2009
Loss of alertness can have dire consequences for people controlling motorized equipment or for people in professions such as defense. Electroencephalogram (EEG) is known to be related to alertness of the person, but due to high level of noise and low signal strength, the use of EEG for such applications has been considered to be unreliable. This study reports the fractal analysis of EEG and identifies the use of maximum fractal length (MFL) as a feature that is inversely correlated with the alertness of the subject. The results show that MFL (of only single channel of EEG) indicates the loss of alertness of the individual with mean (inverse) correlation coefficient = 0.82.
EEG based Brain Alertness Monitoring by Statistical and Artificial Neural Network Approach
International Journal of Advanced Computer Science and Applications
Since several work requires continuous alertness like efficient driving, learning, etc. efficient measurement of the alertness states through neural activity is a crucial challenge for the researchers. This work reports a practical method to investigate the alertness state from electroencephalography (EEG) of the human brain. Here, we have proposed a novel idea to monitor the brain alertness from EEG signal that can discriminate the alertness state comparing resting state with a simple statistical threshold. We have investigated two different types of mental tasks: alphabet counting & virtual driving to monitor their alertness level. The EEG signals are acquired from several participants regarding alphabet counting and virtual motor driving tasks. A 9-channel wireless EEG system has been used to acquire their EEG signals from frontal, central, and parietal lobe of the brain. With suitable preprocessing, signal dimensions are reduced by principal component analysis and the features of the signals are extracted by the discrete wavelet transformation method. Using the features, alertness states are classified using the artificial neural network. Additionally, the relative power of responsible frequency band to alertness is analyzed with statistical inference. We have found that the beta relative power increases at a significant level due to alertness which is good enough to differentiate the alertness state from the control state. It is also found that the increment of beta relative power for virtual driving is much greater than the alphabet counting mental alertness. We hope that this work will be very helpful to monitor constant alertness for efficient driving and learning.