Microsleeps are complete breaks in responsiveness for 0.5–15 s. They can lead to
multiple fatalities in certain occupational fields (e.g., transportation and military) due to the
need in such occupations for extended and continuous vigilance. Therefore, an automated
microsleep detection system may assist in the reduction of poor job performance and
occupational fatalities. An EEG-based microsleep detector offers advantages over a videobased
microsleep detector, including speed and temporal resolution. A series of software
modules were implemented to examine different feature sets to determine the optimal
circumstances for automated EEG-based microsleep detection.
The microsleep detection system was organized in a similar manner to an EEG-based
brain-computer interface (BCI). EEG data underwent baseline removal and filtering to
remove overhead noise. Following this, feature extraction generated spectral features based
upon an estimate of the power spectrum or its logarithmic transform. Following this, feature
selection/reduction (FS/R) was used to select the most relevant information across all the
spectral features. A trained classifier was then tested on data from a subject it had not seen
before. In certain cases, an ensemble of classifiers was used instead of a single classifier. The
performance measures from all cases were then averaged together in leave-one-out crossvalidation
(LOOCV).
Sets of artificial data were generated to test a prototype EEG-based microsleep
detection system, consisting of a combination of EEG and 2-s bursts of 15 Hz sinusoids of
varied signal-to-noise ratios (SNRs) ranging from 16 down to 0.03. The balance between
events and non-events was varied between evenly balanced and highly imbalanced (e.g.,
events occurring only 2% of the time). Features were spectral estimates of various EEG
bands (e.g., alpha band power) or ratios between them. A total of 34 features for each of the
16 channels yielded a total of 544 features. Five minutes of EEG from eight subjects were
used in the generation of the dummy data, and each subject yielded a matrix of 300
observations of 544 features.
Datasets from two prior microsleep studies were employed after validating the system
on the artificial data. The first, Study A (N = 8), had 16 channels sampled at 256 Hz from two
1-hour sessions per subject and the second, Study C (N = 10), had one 50-min session with
30-62 channels per subject sampled at 250 Hz. A vector of 34 spectral features from each
channel was concatenated into a feature vector for each 2-s interval, with each interval having a 1-s overlap with the prior one. In both cases, microsleeps had been identified via a
combination of video recording and performance on a continuous tracking task.
Study A provided four datasets to compare effects of various preprocessing
techniques on performance: (1) Study A bipolar EEG with Independent Component Analysis
(ICA) preprocessing and artefact pruning (total automated rejection of artefact-containing
epochs) and logarithmic transforms of the spectral features (SABIL); (2) Study A bipolar
EEG with ICA-based eye blink removal and artefact removal with pruning of epochs with
major artefacts, and linear spectral features (SABIS); (3) Study A referential EEG
unprocessed by ICA with spectral features (SARUS); and (4) Study A bipolar EEG
unprocessed by ICA with spectral features (SABUS). The second study had one primary
feature set, the Study C referential EEG ICA preprocessed spectral feature (SCRIS) variant.
LOOCV was evaluated based on the phi correlation coefficient.
After replicating prior work, several FS/R and classifier structures were investigated
with both the artificially balanced and unbalanced data. Feature selection/reduction methods
included principal component analysis (PCA), common spatial patterns (CSP), projection to
latent structures (PLS), a new method based on average distance between events and nonevents
(ADEN), ADEN normalized with a z-score transform (ADENZ), genetic algorithms in
concert with ADEN (GADEN), and genetic algorithms in concert with ADENZ (GADENZ).
Several pattern recognition algorithms were investigated: linear discriminant analysis (LDA),
radial basis functions (RBFs), and Support Vector Machines with Gaussian (SVMG) and
polynomial (SVMP) kernels. Classifier structures examined included single classifiers,
bagging, boosting, stacking, and adaptive boosting (AdaBoost).
The highest LOOCV results on artificial data (SNR = 0.3) corresponded to GADEN
with 10 features and a single LDA classifier with a mean phi value of 0.96. Of the four Study
A datasets, PCA with 150 features and a stacking ensemble achieved the highest mean phi of
0.40 with the SABIL feature set, and ADEN with 20 features with a single LDA classifier
achieved the highest mean phi of 0.10 with Study C.
Other machine-learning methodologies, such as training on artificially balanced data,
decreasing the training size, within-subject training and testing, and randomly mixed data
from across subjects, were also examined. Training on artificially balanced data did not
improve performance. An issue found by performing within-subject training and testing was
that, for certain subjects, a classifier trained on one-half of the subject’s data and then tested
on the other half was that classifier performance dropped to random guessing. The low phi values on within-subject tests occurred independently of the feature
selection/reduction method explored. As such, performance of a standard LOOCV was often
dependent on whether a particular testing subject had a low (< 0.15) within-subjects mean phi
correlation coefficient. Training on only the higher mean phi values did not boost
performance. Additional tests found correlations (r = 0.57, p = 0.003 for Study A and r =
0.67, p 0.15) and
longer mean microsleep durations. Other individual subject characteristics, such as number of
microsleeps and subject age, did not have significant differences.
The primary findings highlighted the strengths and limitations of supervised feature
selection and linear classifiers trained upon highly variable between-subject features across
two studies. Findings suggested that a classifier performs best when individuals have high
mean microsleep durations. On the configurations investigated, preprocessing factors, such as
ICA preprocessing, feature extraction method, and artefact pruning, affected the performance
more than changing specific module configurations.
No significant differences between the SABIL features and the lower performing
Study A feature sets were found due to overlapping ranges of performance (p = 0.15). The
findings suggest that the investigated techniques plateaued in performance on the Study A
data, reaching a point of diminishing returns without fundamentally changing the nature of
the classification problem. The different number of channels of varying quality across all
subjects in Study C rendered microsleep classification extremely difficult, but even a linear
classifier can properly generalize if exposed to a large enough variety of data from across the
entire set. Many of the techniques explored are also relevant to other fields, such as braincomputer
interface (BCI) and machine learning
