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.

We hypothesized that temporal features of EEG are altered in sleep apnea subjects comparedto normal subjects. The initial aim was to develop a measure to discriminate sleep stages innormals. The longer-term goal was to apply these methods to identify differences in EEGactivity in sleep apnea subjects from normals. We analyzed the C3A2 EEG and anelectrooculogram (EOG) recorded from 9 normal adults awake and in rapid eye movement(REM) and non-REM sleep. The EEG signals were filtered to remove EOG contamination. Twomeasures of the irregularity of EEG signals, Sample Entropy (SpEn) and Tsallis Entropy, wereevaluated for their ability to discriminate sleep stages. SpEn changes with sleep state, beinglargest in Wake. Stage 3/4 had the smallest SpEn (0.57??0.11) normalized to Wake values,followed by Stage 2 (0.72??0.09), REM (0.75??0.1) and Stage 1 (0.89??0.05). This pattern wasconsistent in all the polysomnogram records analyzed. Similar pattern was observed in leadO1A2 as well. We conclude that SpEn may be useful as part of a montage for assessing sleepstate. We analyzed data from sleep apnea subjects having obstructive and central apnea eventsand have made some preliminary observations; the SpEn values were more similar across sleepstages and also high correlation with oxygen saturation was observed.

Several lines of evidence suggest that asymmetric anterior brain activation is related to affective style, linking left hemisphere activation to positive affect and right hemisphere activation to negative affect. However, previous reports of left frontal hypoactivation in depressed patients were not confirmed in recent studies. This study evaluated additional characteristics of resting EEG alpha (8–13 Hz) asymmetry in 15 clinically depressed patients and 22 healthy adults by recording EEG activity on two separate occasions, 2–4 weeks apart. Across both sessions, group differences in anterior EEG asymmetry were compatible with the original hypothesis. However, groups differed in temporal stability of anterior EEG asymmetry, which was retest reliable in controls but not depressed patients. In contrast, temporal stability of posterior EEG asymmetry was acceptable in both groups. Increased variability of anterior EEG asymmetry may be a characteristic feature for depression, and, if so, this would challenge the notion that anterior EEG alpha asymmetry is a trait marker for depression. / Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.

The recent evolution in multidisciplinary fields of Engineering, neuroscience, microelectronics, bioengineering and neurophysiology have reduced the gap between human and machine intelligence. Many methods and algorithms have been developed for analysis and classification of bio signals, 1 or 2-dimensional, in time or frequency distribution. The integration of signal processing with the electronic devices serves as a major root for the development of various biomedical applications. There are many ongoing research in this area to constantly improvise and build an efficient human- robotic system. Electroencephalography (EEG) technology is an efficient way of recording electrical activity of the brain. The advancement of EEG technology in biomedical application helps in diagnosing various brain disorders as tumors, seizures, Alzheimer’s disease, epilepsy and other malfunctions in human brain. The main objective of our thesis deals with acquiring and pre-processing of real time EEG signals using a single dry electrode placed on the forehead. The raw EEG signals are transmitted in a wireless mode (Bluetooth) to the local acquisition server and stored in the computer. Various machine learning techniques are preferred to classify EEG signals precisely. Different algorithms are built for analysing various signal processing techniques to process the signals. These results can be further used for the development of better Brain-computer interface systems.

Background: The present study was designed to examine prefrontal cortical processes in anxious children that mediate cognitive regulation in response to emotion-eliciting stimuli, and the changes that occur after anxious children participate in a cognitive behavioral therapy treatment program. Methods: Electroencephalographic activity was recorded from clinically anxious children and typically developing children at pre- and post-treatment sessions. Event-related potential components were recorded while children performed a go/no-go task using facial stimuli depicting angry, calm, and happy expressions. Results: At pre-treatment, anxious children had significantly greater posterior P1 and frontal N2 amplitudes than typically developing children, components associated with attention/arousal and cognitive control, respectively. For the anxious group only, there were no differences in neural activation between face (emotion) types or trial (Go vs. No-go) types. Anxious children who did not improve with treatment showed increased cortical activation within the time window of the P1 at pre-treatment relative to comparison and improver children. From pre- to post-treatment, only anxious children who improved with treatment showed increased cortical activation within the time window of the N2. Conclusions: At pre-treatment, anxious children appeared to show increased cortical activation regardless of the emotional content of the stimuli. Anxious children also showed greater medial-frontal activity regardless of task demands and response accuracy. These findings suggest indiscriminate cortical processes that may underlie the hypervigilant regulatory style seen in clinically anxious individuals. Neural activation patterns following treatment suggest that heightened perceptual vigilance, as represented by increased P1 amplitudes for non-improvers, may have prevented these anxious children from learning the treatment strategies, leading to poorer outcomes. Increased cognitive control, as represented by increased N2 amplitudes for improvers, may have enabled these anxious children to implement treatment strategies more effectively, leading to improved treatment outcomes. Hence, P1 activation may serve as a predictor of treatment outcome, while N2 activation may serve as an indicator of treatment-related outcome. These findings point to the cortical processes that maintain maladaptive functioning versus the cortical processes that underlie successful intervention in clinically anxious children.

This dissertation explores the neural coding and mechanisms associated with consciousness by analyzing electrical rhythms of the brain under altered states of consciousness, namely epilepsy and anesthesia. First, transformation of neural coding under epileptogenic conditions is examined by computing the Volterra kernels in a rodent epilepsy model, where the epileptogenic condition is induced by altering the concentrations of Mg2+ and K+ of the perfusate for different levels of excitability. Principal dynamic modes (PDMs) are further deduced from the Volterra kernels to compare the changes in neural dynamics under epileptogenic conditions. The integrating PDMs are shown to dominate at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs shift to higher ranges under epileptogenic conditions, from ripple activities (75 - 200 Hz) to fast ripple activities (200 - 500 Hz). Second, markers of anesthetic states are explored by analyzing amplitude and phase of brain rhythms as well as their interaction and modulation, utilizing electroencephalogram (EEG) recorded from patients undergoing anesthesia. Anesthesia shifts the power to low frequency rhythms, especially alpha rhythms. Additionally anesthesia increases the coupling between alpha rhythms and gamma rhythms while disrupting the coupling between alpha rhythms and ripples (70 - 200 Hz). The results also indicate that the dose responses (i.e. depth of anesthesia) are not necessarily monophasic or linear. The commonality and differences of the changes in brain rhythms associated with these conditions are discussed to elucidate on the possible underlying mechanisms involved in producing consciousness.

The current thesis directly investigated whether changes in the neural correlates of self-regulation (SR) are associated with the effectiveness of treatment for children’s externalizing problems. In order to test this, seventy-one children 8–12 years of age with clinical levels of externalizing behaviour and their parents completed a 12-week cognitive behavioural therapy program (12 sessions) with a parent management training component that was aimed at improving SR. Electroencephalogram (EEG) correlates of SR were evaluated before and after treatment with a go/no-go task requiring inhibitory control on the children. Results showed that event-related potential (ERP) correlates of SR, such as the frontal N2 and frontal P3 event-related potential magnitudes, differed between the clinical sample and a matched comparison group before treatment: the clinical sample had larger N2 magnitudes and smaller frontal P3 magnitudes. Children who showed improvement (45%) with treatment demonstrated a decrease in the magnitude of the N2 in comparison with children who did not improve. For improvers only, source analysis during the time period of the N2 modeled activation decreases in dorsomedial and ventromedial prefrontal cortex as well as the anterior medial temporal lobe. A decrease in N2 magnitudes and corresponding reductions in source activation, in children who improved with treatment, might reflect improved efficiency in the neural mechanisms of SR. These findings may be important steps toward a better identification of neural markers of SR and a better understanding of the mechanisms of treatment efficacy.

This dissertation explores the neural coding and mechanisms associated with consciousness by analyzing electrical rhythms of the brain under altered states of consciousness, namely epilepsy and anesthesia. First, transformation of neural coding under epileptogenic conditions is examined by computing the Volterra kernels in a rodent epilepsy model, where the epileptogenic condition is induced by altering the concentrations of Mg2+ and K+ of the perfusate for different levels of excitability. Principal dynamic modes (PDMs) are further deduced from the Volterra kernels to compare the changes in neural dynamics under epileptogenic conditions. The integrating PDMs are shown to dominate at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs shift to higher ranges under epileptogenic conditions, from ripple activities (75 - 200 Hz) to fast ripple activities (200 - 500 Hz). Second, markers of anesthetic states are explored by analyzing amplitude and phase of brain rhythms as well as their interaction and modulation, utilizing electroencephalogram (EEG) recorded from patients undergoing anesthesia. Anesthesia shifts the power to low frequency rhythms, especially alpha rhythms. Additionally anesthesia increases the coupling between alpha rhythms and gamma rhythms while disrupting the coupling between alpha rhythms and ripples (70 - 200 Hz). The results also indicate that the dose responses (i.e. depth of anesthesia) are not necessarily monophasic or linear. The commonality and differences of the changes in brain rhythms associated with these conditions are discussed to elucidate on the possible underlying mechanisms involved in producing consciousness.

Background: The present study was designed to examine prefrontal cortical processes in anxious children that mediate cognitive regulation in response to emotion-eliciting stimuli, and the changes that occur after anxious children participate in a cognitive behavioral therapy treatment program. Methods: Electroencephalographic activity was recorded from clinically anxious children and typically developing children at pre- and post-treatment sessions. Event-related potential components were recorded while children performed a go/no-go task using facial stimuli depicting angry, calm, and happy expressions. Results: At pre-treatment, anxious children had significantly greater posterior P1 and frontal N2 amplitudes than typically developing children, components associated with attention/arousal and cognitive control, respectively. For the anxious group only, there were no differences in neural activation between face (emotion) types or trial (Go vs. No-go) types. Anxious children who did not improve with treatment showed increased cortical activation within the time window of the P1 at pre-treatment relative to comparison and improver children. From pre- to post-treatment, only anxious children who improved with treatment showed increased cortical activation within the time window of the N2. Conclusions: At pre-treatment, anxious children appeared to show increased cortical activation regardless of the emotional content of the stimuli. Anxious children also showed greater medial-frontal activity regardless of task demands and response accuracy. These findings suggest indiscriminate cortical processes that may underlie the hypervigilant regulatory style seen in clinically anxious individuals. Neural activation patterns following treatment suggest that heightened perceptual vigilance, as represented by increased P1 amplitudes for non-improvers, may have prevented these anxious children from learning the treatment strategies, leading to poorer outcomes. Increased cognitive control, as represented by increased N2 amplitudes for improvers, may have enabled these anxious children to implement treatment strategies more effectively, leading to improved treatment outcomes. Hence, P1 activation may serve as a predictor of treatment outcome, while N2 activation may serve as an indicator of treatment-related outcome. These findings point to the cortical processes that maintain maladaptive functioning versus the cortical processes that underlie successful intervention in clinically anxious children.

The current thesis directly investigated whether changes in the neural correlates of self-regulation (SR) are associated with the effectiveness of treatment for children’s externalizing problems. In order to test this, seventy-one children 8–12 years of age with clinical levels of externalizing behaviour and their parents completed a 12-week cognitive behavioural therapy program (12 sessions) with a parent management training component that was aimed at improving SR. Electroencephalogram (EEG) correlates of SR were evaluated before and after treatment with a go/no-go task requiring inhibitory control on the children. Results showed that event-related potential (ERP) correlates of SR, such as the frontal N2 and frontal P3 event-related potential magnitudes, differed between the clinical sample and a matched comparison group before treatment: the clinical sample had larger N2 magnitudes and smaller frontal P3 magnitudes. Children who showed improvement (45%) with treatment demonstrated a decrease in the magnitude of the N2 in comparison with children who did not improve. For improvers only, source analysis during the time period of the N2 modeled activation decreases in dorsomedial and ventromedial prefrontal cortex as well as the anterior medial temporal lobe. A decrease in N2 magnitudes and corresponding reductions in source activation, in children who improved with treatment, might reflect improved efficiency in the neural mechanisms of SR. These findings may be important steps toward a better identification of neural markers of SR and a better understanding of the mechanisms of treatment efficacy.