EEG-based lapse detection with high temporal resolution.

A warning system capable of reliably detecting lapses in responsiveness (lapses) has the potential to prevent many fatal accidents. We have developed a system capable of detecting lapses in real-time with second-scale temporal resolution. Data was from 15 subjects performing a visuomotor tracking task for two 1-hour sessions with concurrent electroencephalogram (EEG) and facial video recordings. The detector uses a neural network with normalized EEG log-power spectrum inputs from two bipolar EEG derivations, though we also considered a multichannel detector. Lapses, identified using a combination of video rating and tracking behavior, were used to train our detector. We compared detectors employing tapped delay-line linear perceptron, tapped delay-line multilayer perceptron (TDL-MLP), and long short-term memory (LSTM) recurrent neural networks operating continuously at 1 Hz. Using estimates of EEG log-power spectra from up to 4 s prior to a lapse improved detection compared with only using the most recent estimate. We report the first application of a LSTM to an EEG analysis problem. LSTM performance was equivalent to the best TDL-MLP network but did not require an input buffer. Overall performance was satisfactory with area under the curve from receiver operating characteristic analysis of 0.84 +/- 0.02 (mean +/- SE) and area under the precision-recall curve of 0.41 +/- 0.08.

Detection of lapses in responsiveness from the EEG.

A system capable of reliably detecting lapses in responsiveness ('lapses') has the potential to increase safety in many occupations. We have developed an approach for detecting the state of lapsing with second-scale temporal resolution using data from 15 subjects performing a one-dimensional (1D) visuomotor tracking task for two 1 h sessions while their electroencephalogram (EEG), facial video, and tracking performances were recorded. Lapses identified using a combination of facial video and tracking behaviour were used to train the classification models. Linear discriminant analysis was used to form detection models based on individual subject data and stacked generalization was utilized to combine the outputs of multiple classifiers to obtain the final prediction. The performance of detectors estimating the lapse/not-lapse state at 1 Hz based on power spectral features, approximate entropy, fractal dimension, and Lempel-Ziv complexity of the EEG was compared. Best lapse state estimation performance was achieved using the detector model created using power spectral features with an area under the curve from receiver operating characteristic analysis of 0.86 ± 0.03 (mean±SE) and an area under the precision-recall curve of 0.43 ± 0.09. A novel technique was developed to provide improved estimation of accuracy of detection of variable-duration events. Via this approach, we were able to show that the detection of lapse events from spectral power features was of moderate accuracy (sensitivity = 73.5%, selectivity = 25.5%).

Lapses of responsiveness: Characteristics, detection, and underlying mechanisms.

Lapses in responsiveness ('lapses'), particularly microsleeps and attention lapses, are complete disruptions in performance from approximately 0.5-15 s. They are of particular importance in the transport sector in which there is a need to maintain sustained attention for extended periods and in which lapses can lead to multiple-fatality accidents.

Event-based detection of lapses of responsiveness.

EEG spectral power has been shown to correlate with level of arousal and alertness in humans. In this paper, we assess its usefulness in the detection of lapses of responsiveness ('lapses') on an event, rather than state, basis. Eight non-sleep-deprived normal subjects performed two 1-hour sessions of a continuous tracking task while EEG and facial video were recorded. Lapses were identified by the presence of tracking flat spots or clear instances of behavioural microsleeps as identified by a human rater observing video recordings of the subject. Spectral power in the standard EEG bands was calculated using the Burg method on 16 bipolar derivations to form an EEG feature matrix. Linear discriminant analysis was used to form a classifier for each subject. The 8 classifiers were combined using stacked generalization with constrained-least squares fitting to create an overall detection model. Estimation of lapse-event detection performance required the development of a novel procedure to account for the variable duration of lapses. Event detection for the concatenated data from all 8 subjects yielded an overall sensitivity of 73.5%, selectivity of 25.5%, and accuracy of 61.2%. While the performance of this detector is modest, and not yet sufficient for real-time detection, the detection of lapses at such a high temporal resolution (1 s) is encouraging relative to previous studies which have generally tended to estimate changes in alertness on a minute-scale basis.

Frequent lapses of responsiveness during an extended visuomotor tracking task in non-sleep-deprived subjects.

We investigated the occurrence of lapses of responsiveness (lapses) in 15 non-sleep-deprived subjects performing a 1D continuous tracking task during normal working hours. Tracking behaviour, facial video, and electroencephalogram (EEG) were recorded simultaneously during two 1-h sessions. Rate and duration were estimated for lapses identified by a tracking flat spot and/or video sleep. Fourteen of the 15 subjects had one or more lapses, with an overall rate of 39.3 +/- 12.9 lapses per hour (mean +/- SE) and a lapse duration of 3.4 +/- 0.5 s. We also found that subjects' performance improved towards the end of the 1-h long session, even though no external temporal cues were available. Spectral power was found to be higher during lapses in the delta, theta, and alpha bands, and lower in the beta, gamma, and higher bands, but correlations between changes in EEG power and lapses were low. In conclusion, lapses are a frequent phenomenon in normal subjects - even when not sleep-deprived - engaged in an extended monotonous continuous visuomotor task. This is of particular importance to the transport sector in which there is a need to maintain sustained attention for extended periods of time and in which lapses can lead to multiple-fatality accidents.