Malik T R Peiris

New Zealand Brain Research Institute, Christchurch, Canterbury Region, New Zealand

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Publications (11)8.67 Total impact

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    ABSTRACT: 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%).
    Journal of Neural Engineering 01/2011; 8(1):016003. · 3.28 Impact Factor
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    ABSTRACT: 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.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:1788-91.
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    ABSTRACT: 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.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2008; 2008:4960-3.
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    P.R. Davidson, R.D. Jones, M.T.R. Peiris
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    ABSTRACT: 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 plusmn 0.02 (mean plusmn SE) and area under the precision-recall curve of 0.41 plusmn 0.08
    IEEE Transactions on Biomedical Engineering 06/2007; · 2.35 Impact Factor
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    ABSTRACT: 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.
    Journal of Sleep Research 10/2006; 15(3):291-300. · 3.04 Impact Factor
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    ABSTRACT: 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 behavioral microsleeps (BMs). Eight non-sleep-deprived normal subjects performed two 1-hour sessions of a continuous tracking task while EEG and facial video were recorded. BMs were identified independent of tracking performance by a human rater by viewing the video recordings. Spectral power, normalized spectral power, and power ratios in the standard EEG bands were calculated using the Burg method on 16 bipolar derivations to form an EEG feature matrix. PCA was used to reduce the dimensionality of the feature matrix and linear discriminant analysis used to form a classifier for each subject. The 8 classifiers were combined using stacked generalization to create an overall detection model and N-fold cross-validation used to determine its performance (Phi=0.30 +/- 0.05, mean +/- SE). While modest, the detection of BMs at such a high temporal resolution (1 s) has not been achieved previously other than by our group.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2006; 1:5723-6.
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    ABSTRACT: The fractal dimension (FD) of EEG has been shown to be of value in the detection of epileptic seizures. In this paper, we assess its usefulness in detecting behavioural microsleeps. Fifteen non-sleep-deprived normal subjects performed two 1-hour sessions of a continuous tracking task while EEG, EOG and facial video were recorded. Higuchi's algorithm was used to calculate the FD of the EEG. Video lapses were scored independently from tracking performance by a human rater. A subset of data was rated independently by three human raters observing both tracking performance and the video rating to identify behavioural microsleep events. The mean point-biserial correlation between FD and the mean human rating was -0.213 indicating modest agreement. Crossvalidated detection performance of the FD was poor with a mean correlation (.. = -0.099). This suggests that, on its own, FD of the EEG is unlikely to be useful for detecting microsleeps.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2005; 6:5742-5.
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    ABSTRACT: It is critically important for certain occupational groups to remain highly alert throughout their working day. For safety reasons, it would be useful to automatically detect lapses in performance using EEG/EOG. Automating the detection process could be simplified considerably if we could mimic human experts. Surprisingly, it is unclear to what extent human EEG raters are able to detect lapses. Consequently, we undertook a study in which 4 expert EEG raters assessed the level of alertness of 10 air traffic controllers by observing a combination of their EEG and EOG while they performed a 10 min psychomotor vigilance task (PVT). They were specifically required to identify lapses or sleep episodes that might lead to a lapse in PVT performance. A reaction time .. 500 ms was defined as a PVT lapse. There was a total of 101 lapses (mean duration = 1.00 s). Of these, only 6 lapses were detected by one or more raters and all of these were marked as ;sleep'. Overall the human expert raters were unable to reliably identify lapses based only on EEG and EOG. This poor performance suggests an automated system would need to identify subtle features not overtly visible in the EEG.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2005; 6:5735-7.
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    P R Davidson, R D Jones, M T Peiris
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    ABSTRACT: Lapses in visuomotor performance are often associated with behavioral microsleep events. Experiencing a lapse of this type while performing an important task can have catastrophic consequences. A warning system capable of reliably detecting patterns in EEG occurring before or during a lapse has the potential to save many lives. We are developing a behavioral microsleep detection system which employs Long Short'Term Memory (LSTM) recurrent neural networks. To train and validate the system, EEG, facial video and tracking data were collected from 15 subjects performing a visuomotor tracking task for 2 1-hour sessions. This provided behavioral information on lapse events with good temporal resolution. We developed an automated behavior rating system and trained it to estimate the mean opinion of 3 human raters on the likelihood of a lapse. We then trained an LSTM neural network to estimate the output of the lapse rating system given only EEG spectral data. The detection system was designed to operate in real-time without calibration for individual subjects. Preliminary results show the system is not reliable enough for general use, but results from some tracking sessions encourage further investigation of the reported approach.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2005; 6:5754-7.
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    ABSTRACT: In many high-risk occupations, it is critical that a person remains alert at all times. There is much to be gained by being able to monitor a person on-line and detect lapses of consciousness (LoC) so that remedial action can be taken (e.g., a rest break) to ensure that safety is maintained. In this study, 15 normal subjects were observed on two sessions while they performed a continuous tracking task for a period of 1 hour. EEG, eye movements, tracking performance data and a video of the subject were recorded during the session. This work presents some preliminary results on the phenomenon of lapsing. Only 4 of the 15 subjects did not have a LoC at some stage. Seven subjects had LoCs more than 45 times and 4 more than 100 times during the 2 hours. The mean rate of lapsing over all subjects was 29.1 LoC/h. In contrast, lapses in performance were caused by both lapses of consciousness (30.1%) and attention (69.9%). There was no correlation found between age of subject and number of lapses of consciousness.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2004; 7:4721-4.
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    Malik Tivanka Rajiv Peiris
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    ABSTRACT: Performance lapses in occupations where public safety is paramount can have disastrous consequences, resulting in accidents with multiple fatalities. Drowsy individuals performing an active task, like driving, often cycle rapidly between periods of wake and sleep, as exhibited by cyclical variation in both EEG power spectra and task performance measures. The aim of this project was to identify reliable physiological cues indicative of lapses, related to behavioural microsleep episodes, from the EEG, which could in turn be used to develop a real-time lapse detection (or better still, prediction) system. Additionally, the project also sought to achieve an increased understanding of the characteristics of lapses in responsiveness in normal subjects. A study was conducted to determine EEG and/or EOG cues (if any) that expert raters use to detect lapses that occur during a psychomotor vigilance task (PVT), with the subsequent goal of using these cues to design an automated system. A previously-collected dataset comprising physiological and performance data of 10 air traffic controllers (ATCs) was used. Analysis showed that the experts were unable to detect the vast majority of lapses based on EEG and EOG cues. This suggested that, unlike automated sleep staging, an automated lapse detection system needed to identify features not generally visible in the EEG. Limitations in the ATC dataset led to a study where more comprehensive physiological and performance data were collected from normal subjects. Fifteen non-sleep-deprived male volunteers aged 18-36 years were recruited. All performed a 1-D continuous pursuit visuomotor tracking task for 1 hour during each of two sessions that occurred between 1 and 7 weeks apart. A video camera was used to record head and facial expressions of the subject. EEG was recorded from electrodes at 16 scalp locations according to the 10-20 system at 256 Hz. Vertical and horizontal EOG was also recorded. All experimental sessions were held between 12:30 and 17:00 hours. Subjects were asked to refrain from consuming stimulants or depressants, for 4 h prior to each session. 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. The study also showed that lapsing and tracking error increased during the first 30 or so min of a 1-h session, then decreased during the remaining time, despite the absence of external temporal cues. EEG 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. Thus, complete lapses in responsiveness are a frequent phenomenon in normal subjects - even when not sleep-deprived - undertaking an extended, monotonous, continuous visuomotor task. This is the first study to investigate and report on the characteristics of complete lapses of responsiveness during a continuous tracking task in non-sleep-deprived subjects. The extent to which non-sleep-deprived subjects experience complete lapses in responsiveness during normal working hours was unexpected. Such findings will be of major concern to individuals and companies in various transport sectors. Models based on EEG power spectral features, such as power in the traditional bands and ratios between bands, were developed to detect the change of brain state during behavioural microsleeps. Several other techniques including spectral coherence and asymmetry, fractal dimension, approximate entropy, and Lempel-Ziv (LZ) complexity were also used to form detection models. Following the removal of eye blink artifacts from the EEG, the signal was transformed into z-scores relative to the baseline of the signal. An epoch length of 2 s and an overlap of 1 s (50%) between successive epochs were used for all signal processing algorithms. Principal component analysis was used to reduce redundancy in the features extracted from the 16 EEG derivations. Linear discriminant analysis was used to form individual classification models capable of detecting lapses using data from each subject. The overall detection model was formed by combining the outputs of the individual models using stacked generalization with constrained least-squares fitting used to determine the optimal meta-learner weights of the stacked system. The performance of the lapse detector was measured both in terms of its ability to detect lapse state (in 1-s epochs) and lapse events. Best performance in lapse state detection was achieved using the detector based on spectral power (SP) features (mean correlation of φ = 0.39 ± 0.06). Lapse event detection performance using SP features was moderate at best (sensitivity = 73.5%, selectivity = 25.5%). LZ complexity feature-based detector showed the highest performance (φ = 0.28 ± 0.06) out of the 3 non-linear feature-based detectors. The SP+LZ feature-based model had no improvement in performance over the detector based on SP alone, suggesting that LZ features contributed no additional information. Alpha power contributed the most to the overall SP-based detection model. Analysis showed that the lapse detection model was detecting phasic, rather than tonic, changes in the level of drowsiness. The performance of these EEG-based lapse detection systems is modest. Further research is needed to develop more sensitive methods to extract cues from the EEG leading to devices capable of detecting and/or predicting lapses.

Publication Stats

85 Citations
8.67 Total Impact Points

Institutions

  • 2005–2011
    • New Zealand Brain Research Institute
      Christchurch, Canterbury Region, New Zealand
  • 2007–2010
    • Canterbury District Health Board
      • Department of Medical Physics and Bioengineering
      Christchurch, Canterbury, New Zealand
  • 2004
    • University of Canterbury
      • Department of Electrical and Computer Engineering
      Christchurch, Canterbury, New Zealand