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The ability to react to events in the external world determines the fate of every living organism, this general state of readiness is called as 'alertness'. What happens to neurodynamics in the brain when alertness fades away as we fall asleep? How is behaviour affected? These questions will help us understand the organizing principles in the brain and functions of sleep itself. Here I use two distant animal models, the richness in behaviour and complexity of the human brain to understand how alertness transitions affects attention; and the experimental flexibility of the fruit fly, to understand its effect over longer time intervals. I first develop an objective method to track alertness using Electroencephalography (EEG). Then, I investigate the behavioural dynamics using an auditory spatial attention task while participants fall asleep. By using multilevel modelling and psychophysics, I show that participants systematically misclassify tones from the left side when drowsy, and further with a hierarchical drift diffusion model (HDDM) show how drift-rate (evidence accumulation) explains errors. Then, I show convergent evidence in the neural dynamics using multivariate pattern analysis (MVPA). Next, I probe the effect of handedness on the same task. Handedness affects behaviour only under drowsy condition and I show how neural dynamics are affected by a combination of handedness and alertness. To approach alertness transitions in a system with reduced neural complexity, I explore those dynamics in the fruit fly (Drosophila melanogaster), using both single and multichannel local field potential (LFP) data to show how alertness transitions and sleep modulate different regions of the fly brain. Further, I validate the results by converging evidence from causal manipulations. Finally, I discuss how the mapping of alertness transitions -under natural conditions- can help us understand fundamental questions in neuroscience such as the functions of sleep or the mechanisms of general anaesthesia.

In this dissertation the effects of variations in stimulus properties and CTOA, in auditory attention tasks were explored using recently developed approaches to EEG analysis including LIMO. The last experiment was structured using information theory, designing an effective experiment. Four studies were carried out using a number parity decision task, that employed different combinations of cueing Tone (T), Novel (N) and the Goal (G) stimuli. In the first EEG study, contrary to previous findings (Polich 2002, 2007) in control participants, no correlation between the time of a novel condition to the next novel condition and P300 amplitude was found. Therefore single trial across-subject averaging of participants’ data revealed significant correlations (r > .3) of stimulus properties (such as probability, frequency, amplitude and duration) on P300, and even r > .5 was found when N was an environmental sound in schizophrenic patients. In the second EEG study, simultaneously with fMRI recordings, the participants that showed significant behavioural distraction evoked brain activations and differences in both hemispheres (similar to Corbetta, 2002, 2008) while the participants, as a whole, produced significant activations mainly in left cortical and subcortical regions. A context analysis was run in distracted participants contrasting the trials immediately prior to the G trials, resulting in different prefrontal activations, which was consistent with studies of prefrontal control of visual attention (Koechlin 2003, 2007). In the third EEG study, the distractor noise type was manipulated (white vs environmental sounds) as well as presence or absence of scanner background noise in a blocked design. Results showed consistent P300, MMN and RON due to environmental noise. In addition, using time constants found in MEG results (Lu, Williamson & Kaufman, 1992) and adding the CTOA to the analysis, an information theory framework was calculated. After the simulation of the information of the experiment, a saddle indentation in the curve of the information measure based on the states of the incoming signal at around 300 ms CTOA was found. This saddle indentation was evident in more than 60 novel trials. In the fourth study, the CTOA and stimulus properties were manipulated in a parametric experiment. Based on the three studies, reducing complexity if the task (first study), using more than 60 stimuli in the novel conditions (third study). The CTOA randomly varying between 250 ms or 500 ms. Thirty-eight ANCOVA with 2 categorical and 1 continuous regressors were conducted and determined which time and channels elicited reliably signatures (p <.05) in the whole participants at short CTOA. Results revealed differences for the waveforms of current condition by depending on which condition appeared previously as well in terms of frequency and duration in scalp frontal electrodes (such as the second study). These results were interpreted as a consequence of switching between modes of attention and alerting states which resulted in the activation of frontal areas. Moreover, contextual analyses showed that systematic manipulation of stimulus properties allowed the visualization of the relationships between CTOA, executive function and orienting of attention.

Brain-computer interfaces (BCIs) are a means of controlling devices in our environment using brain signals. They are promising tools for persons with neurological disabilities including speech and muscular impediments that externally connect the brain and affected muscle groups using computational devices. There are several brain imaging modalities categorized as invasive or non-invasive with electroencephalography (EEG) being the most popular due to its practicality and affordability. A BCI pipeline consists of data acquisition, preprocessing, feature extraction, classification followed by an action by a device. There are multiple machine learning (ML) algorithms that can be used for classification however there are no guidelines for selecting an algorithm for a certain control signal or BCI application. In this thesis, we compare five common ML algorithms, namely Logistic regression (LR), neural networks (NNs), support vector machines (SVMs), k-nearest neighbor (KNN) and a Bayesian classifier for two common control signals used in EEG-BCIs, the P300 potential and sensorimotor rhythms. For P300, the Bayesian decoder and a fully connected NN performed the best for cross-validations performed on each session's data. SVM performed the best for leave-one-subject-out cross-validations where the training data didn't include any of the test subject's data. SVM and LR performed the best for sensorimotor data for cross-validations on each session. A procedure mimicking real-time decoding performed on the sensorimotor data didn't differentiate between the algorithms in terms of accuracy however a recurrent neural network based on long short term memory units had the lowest lags defined as the temporal offset between the predictions and true labels. These results provide a good foundation for the selection of ML algorithms for BCI applications. Future works can build on this by incorporating more ML algorithms and testing on additional BCI control signals.

Stress has been a prevalent part of modern life, particularly during the time of the pandemic. While short-term stress may cause little harm to productivity, if left untreated for a long period of time, it could eventually lead to anxiety and depression, which significantly decrease the quality of life. Such a problem is even more severe among students. A recent survey conducted in 2020 with 15,346 graduate and professional students had shown that 32% of them screened positive for major depressive disorder. This study, which took place in a real-world classroom, aims to uncover some of the neuronal mechanisms behind stress among young adults with their recorded EEG data. Such understanding could provide the theoretical foundation for stress reduction and prevention techniques such as real-time stress detection and non-invasive neurostimulation. This thesis is structured into four chapters. In the first chapter, the author introduced the importance of the problem, the experiment design, and the dataset. In the next chapter, the author began the stress analysis by studying the power spectral density of the 30 EEG electrodes. Results showed that most theta (4-8Hz) and alpha (8-13Hz) frequency bands in the frontal, central, and right parietal regions showed statistical significance among the elevated and normal stress groups. While the power spectra information is helpful for understanding stress, it is important to remember that EEG signals are mixtures of source activities, which makes the underlying source activities and locations unknown. Thus, in chapter three, the author decomposed the EEG data into independent sources using Independent Component Analysis (ICA) and analyzed the effect of stress in terms of cortical source activities. The results showed that some sources responded to stress while others did not. One limitation of this study was that each source was analyzed individually. Thus, in the final chapter, the author focused on exploring the interactions between regions (i.e. effective connectivity) under stress. The results showed that the information inflow and outflow near the central region were statistically different between the elevated stress and normal stress groups.

The emergence of virtual personal assistants like Apple Siri, Amazon Alexa, Google Assistant, Samsung Bixby, Windows Cortana, etc has improved the user experience for smartphone and personal computer users. The automatic speech recognition (ASR) system forms an important component of a virtual assistant. An ASR system converts speech signals into text which is further processed by the natural language understanding (NLU) component of the virtual assistant. However, the performance of an ASR system degrades in presence of background noise and this affects the performance of virtual assistants in noisy environments like shopping mall or airport. On the other hand, studies have demonstrated that we humans are able to perform speech recognition with a lower word error rate (WER) compared to machines in presence of background noise. This motivated me to investigate how to use non-invasive electroencephalography (EEG) brain signal features recorded synchronously with noisy speech to improve the performance of ASR and other speech processing models. Current state-of-the-art ASR systems are trained to recognize only acoustic features and this limits technology accessibility for people with speaking disabilities. This motivated me to investigate techniques to design ASR systems capable of recognizing EEG features with no speech input. We, humans, have a high speech rate of 150 words per minute, thus an EEG speech prosthetic that works by first recognizing text from EEG signal and then generating speech from the recognized text using a state-of-the-art speaker dependent or independent text-to-speech (TTS) system, may suffer high latency. This motivated me to investigate algorithms to generate speech signals directly from EEG signals instead of translating EEG signals to text. First, In this thesis, we demonstrate a neural network-based algorithm to improve the performance of ASR and voice activity detection (VAD) systems operating in presence of background noise on a limited English vocabulary using EEG features. We also show that EEG features can be used to improve end-pointer detection model performance as an extension of VAD application. Second, In this thesis we demonstrate a neural network-based algorithm to perform isolated speech recognition using only EEG features with no speech with high accuracy on a limited English vocabulary and we then study three techniques inspired by representation learning to improve the performance of continuous speech recognition systems using only EEG features with no speech. Third, In this thesis, we study different techniques to generate speech features from EEG features and vice-versa. We study a recurrent neural network (RNN) based regression model with and without attention layer to generate acoustic features from EEG features. We demonstrate generating acoustic features from EEG features with low test time error rates using the RNN model with and without attention layer. We show that for majority of our experiments attention model outperformed the RNN model without attention layer in terms of test time error rates even though for some subjects adding attention layer to the model was not helpful. We further identified the right sampling frequency and acoustic feature dimension to generate audio waveform with broader characteristics closer to the ground truth audio waveform from EEG signals. Fourth, In this thesis, we demonstrate an algorithm to improve the speech recognition performance for aphasia, apraxia and dysarthria speech by utilizing EEG features. We demonstrate that our proposed algorithm in real-time can make use of ear EEG, dry EEG and acoustic features to outperform a baseline speech recognition model trained using only acoustic features when tested on several subjects with various severity levels of aphasia, apraxia and dysarthria. We further show that the algorithm can be extended to other tasks like speaker identification and voice activity detection for impaired speech. We demonstrate that the proposed algorithm can also be used to improve the performance of speech recognition systems operating in presence of background noise in addition to impaired speech.

Human language has the unique characteristic where we can create infinite and novel phrases or sentences; this stems from the ability of composition, which allows us to combine smaller units into bigger meaningful units. Composition involves us following syntactic rules stored in memory and building well-formed structures incrementally. Research has shown that neural circuits can be associated with cognitive faculties such as memory and language and there is evidence indicating where and when the neural indices of the processing of composition are. However, it is not yet clear "how" neural circuits actually implement compositional processes. This dissertation aims to probe "how" composition of meaning is represented by neural circuits by investigating the role of low-frequency neural activity in carrying out composition. Neuroelectric signals were recorded with Electroencephalography (EEG) to examine the functional interpretation of low-frequency neural activity in the so-called delta band of 0.5 to 3 Hz. Activities in this band have been associated with the processing of syntactic structures (Ding et al. 2016). First, whether these activities are indeed associated with hierarchy remains under debate. This dissertation uses a novel condition in which the same words are presented, but their order is changed to remove the syntactic structure. Only entrainment with syllables was found in this "reversed" condition, supporting the hypothesis that neural activities in the delta band entrain to abstract syntactic structures. Second, we test the timing for language users to combine words and comprehend sentences. How comprehension correlates with this low-frequency neural activity and whether it represents endogenous neural response or evoked response remains unclear. This dissertation manipulates the length of syllables and regularity between syllables to test the hypotheses. The results support the view that this neural activity reflects endogenous response and suggest that it reflects top-down processing. Third, what semantic information modulates this low-frequency neural activity is unknown. This dissertation examines several semantic variables typically associated with different aspects of semantic processing. The stimuli are created by varying the statistical association between words, world knowledge, and the conceptual results of semantic composition. The current results suggest that low-frequency neural activity is not driven by semantic processing. Based on the above findings, we propose that neural activities in the delta band reflect top-down predictive processing that involves syntactic information directly but not semantic information.

In recent years, there has been a significant increase in mindfulness-related techniques. The willingness to experience emotions fully, just as they are, is a catalyst for psychological change to occur. Mindfulness has been used to alleviate stress and promote healthy living (Lagopoulos et al., 2009). The clinical utility of mindfulness has grown exponentially; however, the neuropsychological process of mindfulness is still being understood. The present study sought to examine the influence of a brief mindfulness intervention on brain activation in college students. A sample (n = 17) of undergraduate participants were randomly assigned into a prepost mindfulness intervention or control group. Both groups received neurophysiological measures: Electroencephalogram (EEG), Electrocardiogram (EKG), and Galvanic Skin Response (GSR/EDA Electrodermal Activity), and self-report measures: Adverse Childhood Experiences (ACEs), Patient Health Questionnaire, Ninth Edition (PHQ-9), and Five Facet Mindfulness Questionnaire (FFMQ). Participants in the intervention group completed daily, independent mindfulness practices via phone application (Headspace; developed by Puddicombe & Pierson, 2010) in conjunction with weekly mindfulness group meetings for 4 weeks. The control group met and watched TED talk videos for an equivalent frequency and duration. ACEs was used as a covariate. There were no statistically significant findings for the EEG results, likely due to the small sample size. Analysis of effect sizes indicate the intervention group experienced reduced activation in emotional reactivity of the limbic system post-intervention. Furthermore, there were frontal lobe responses showing a reduction in negative emotions aroused by the stimulus task. Results of self-report measures from the PHQ-9 and FFMQ did not produce an interaction or main effect. However, analysis of the FFMQ subscales indicated that both groups demonstrated improvement in the non-reactivity and non-judging domains of the FFMQ. Limitations and recommendations for future research are discussed.

Brain-computer interface (BCI) systems read and infer brain activity directly from the brain through for instance electroencephalography (EEG), while bypassing the common neuromuscular pathways. These systems can provide assistive devices for communication and locomotion, interventions for motor restoration and rehabilitation, as well as neurofeedback training for cognitive enhancement. Motor imagery-based BCIs are a category of BCI systems that allow a user to generate control commands by imagining moving different body parts (such as the right or left hand) and the goal of the BCI is to correctly detect the imagined body part from EEG patterns. The ability of users to generate discriminable motor imagery-based control signals is very variable and environmental effects such as other brain processes affect current BCI systems to the point that they are mostly limited to lab environments. This dissertation proposes novel machine learning and design methods to improve the performance and reliability of motor imagery EEG-based brain-computer interfaces to provide patients in need, the ability of independent interaction with the outside world. This dissertation proposes: 1) a more elaborate feedback paradigm to allow users to better learn how to generate discriminable motor imagery signal, 2) a novel hybrid motor imagery BCI utilizing the user's brain activity in response to the BCI output (feedback) with improved performance and reliability compared to existing motor imagery BCI systems, 3) an artificial neural network architecture to capture spatio-temporal aspects of the motor imagery signal to improve classification of motor imagery activity, and 4) a novel covariance-based method that uses Riemannian and Euclidean geometry to capture spatial and temporal aspects of the feedback-related brain activity in response to BCI error to further improve hybrid BCIs that utilize this type of brain activity . By giving improved training feedback and better utilizing the available brain signals, the proposed methods improve the performance and reliability of motor imagery BCIs and provide the chance to greatly increase the number of people who are successfully able to operate one. The developed techniques could also be useful for discovering and training other mental commands that could be used in EEG-based BCIs not limited to motor imagery BCIs.

Investigation of the influence of reward and reward-related cues on behaviour has a longstanding tradition in animal research and dates back to typical Pavlovian and operant conditioning experiments from famous behaviourists such as Thorndike, Skinner and Pavlov. However, recent developments in neuroimaging (i.e. new imaging and analysis techniques) and new concepts from neuroeconomics and decision making have led to the field’s rise in popularity in human neuroscience. This thesis investigated how reward and reward-predicting cues influence: 1) human motor behaviour / control and 2) goal-directed / motivated behaviour. Understanding the influence of reward on these behavioural domains will help develop our understanding of psychiatric diseases characterised by impaired reward and / or learning processes (i.e. autistic spectrum disorder, addictive behaviour), and improve motor performance in sports or rehabilitation. The first study investigated how reward can be used to modulate a phenomenon called motor slowing (i.e. decrease / slowing in maximal finger tapping speed over time). Behavioural data were combined with neuroimaging data (electroencephalography (EEG), functional magnetic resonance imaging (fMRI)) to establish the neurophysiological correlates / markers of this phenomenon. We show that reward decreases motor slowing significantly by increasing neural activity within the motor network. These findings stress the strong link between the reward and motor system, putatively via a dopaminergic pathway. The second study investigated the influence of reward-predicting cues on goal-directed behaviour by using an experimental paradigm called Pavlovian-to-instrumental transfer (PIT). Multiple types of reward, such as money, food, or social approval, are capable of driving human behaviour. However, most previous investigations have only focused on one of these reward classes in isolation, and even fewer have equated the value of different reward classes. Therefore it is not clear whether different reward classes have a unique influence on behaviour or whether the subjective value of the reward, rather than reward type per se, is most important for driving behaviour. Our findings suggest that individually matched monetary, food and social rewards are equally effective as appetitive reinforcers. These findings strengthen the hypotheses that subjective value is crucial for determining how much reward-related stimuli influence behaviour. In a third study we apply a similar experimental paradigm (i.e. PIT) to a clinical population, and additionally measure the participant’s eye movements. Obese individuals have been shown to exhibit abnormal sensitivity to rewards and reward-predicting cues, such as food-associated cues frequently used in advertisements. It has also been shown that food-associated cues can increase goal-directed behaviour but it is unknown whether this effect differs between normal-weight, overweight, and obese individuals. Our results suggest that overweight individuals are more susceptible to food-related cues than normal-weight controls and obese individuals. These findings might also have clinical implications, for example, in targeting overweight individuals with a behavioural therapy (e.g. extinction training, attentional control training, response training). Finally, we discuss the practical relevance of our results and suggest future directions for the two lines of research. To conclude, rewards and reward-predicting cues strongly influence human motor behaviour / control and goal-directed / motivated behaviour, which makes it relevant for practical applications and further basic / translational research.

In recent years, brain computer interfaces (BCIs) have gained popularity in non-medical domains such as the gaming, entertainment, personal health, and marketing industries. A growing number of companies offer various inexpensive consumer grade BCIs and some of these companies have recently introduced the concept of BCI "App stores" in order to facilitate the expansion of BCI applications and provide software development kits (SDKs) for other developers to create new applications for their devices. The BCI applications access to users' unique brainwave signals, which consequently allows them to make inferences about users' thoughts and mental processes. Since there are no specific standards that govern the development of BCI applications, its users are at the risk of privacy breaches. In this work, we perform first comprehensive analysis of BCI App stores including software development kits (SDKs), application programming interfaces (APIs), and BCI applications w.r.t privacy issues. The goal is to understand the way brainwave signals are handled by BCI applications and what threats to the privacy of users exist. Our findings show that most applications have unrestricted access to users' brainwave signals and can easily extract private information about their users without them even noticing. We discuss potential privacy threats posed by current practices used in BCI App stores and then describe some countermeasures that could be used to mitigate the privacy threats. Also, develop a prototype which gives the BCI app users a choice to restrict their brain signal dynamically.

Significant differences have been found in neural connectivity of the brain in frontal, central, temporal, and parietal areas of individuals who experienced childhood trauma compared to those who had not (Cook, Ciorciari, Varker, & Devilly, 2009). This study investigated the relationship between the number of distressing and traumatic life experiences and participants’ neural responses to observing simulated conflict in intimate relationship. Graduate students (n = 11) answered conflict resolution and emotional activation questions while watching a simulated, escalating marital conflict. The participants’ neural responses were recorded via EEG mean power data from frontal and temporal brain regions. Heart rate (bpm) and galvanic skin response (gsr) were also collected. Participants completed questionnaires (SRRS, LEC-5) in order to identify trauma (experimental) and non-trauma (control) groups. Results indicated a significant interaction between groups. A main effect for conditions and channels was also found. Results within the experimental group suggested brain activation decreased in response to stimuli, demonstrating the possibility of emotional centers shutting down in response to viewing conflict.

There is evidence to suggest that the distinct psychotic symptoms of schizophrenia emerge because of reduced connectivity between frontal and temporal brain regions. According to theory, this reduced connectivity leads to insufficient suppression of temporal lobe structures during self-generated mentation and actions, which causes the resulting sensations to be misperceived as externally generated. Psychotic-like experiences have been argued to exist on a continuum throughout the population such that non-clinical individuals can experience low to intermediate levels of psychotic-like experiences. According to this concept –commonly referred to as ‘schizotypy’ – people suffering from schizophrenia simply represent the extreme end of this continuum. This thesis has four primary aims: 1.) To use electroencephalography (EEG) to investigate whether non-clinical individuals who score highly on schizotypy show the same electrophysiological suppression abnormalities as have repeatedly been observed in schizophrenia patients, and thereby provide empirical support for the concept of a neurophysiological continuum of psychosis. 2.) To use Diffusion Tensor Imaging (DTI) to investigate whether sensory suppression deficits in patients with schizophrenia are due to loss of white matter coherence in fiber bundles connecting frontal and temporal brain regions. 3.) To identify the relationship between white matter abnormalities in schizophrenia and psychotic symptoms such as auditory verbal hallucinations and delusions in patients with schizophrenia. 4.) To use free-water imaging to differentiate actual white matter degeneration from extracellular processes (such as neuroinflammation) in patients with schizophrenia. This research is crucially important, as understanding and elucidating the underlying causes of the characteristic symptoms of schizophrenia is a necessary first step for the development of more effective treatments. The results from the EEG studies (chapters 2 and 3) suggest that non-clinical, highly schizotypal individuals exhibit subnormal levels of N1-suppression to self-generated speech and undelayed, button-press-elicited sounds, which can be ameliorated by imposing a sub-second delay between the button-press and the sound. To the extent that these findings are similar to the N1-suppression abnormalities which have previously been reported in patients with schizophrenia they provide empirical support for the existence of a neurophysiological ‘continuum of psychosis’. Findings from the DTI studies (chapters 4 to 6) provide evidence for a relationship between structural integrity loss and psychotic symptoms in schizophrenia and indicate that active psychotic states may be associated with neuroinflammation. Additionally, the results of the DTI studies suggest that actual white matter degeneration, independent of extracellular processes, in chronic schizophrenia might be more localized than suggested in previous studies. Taken together, the findings from this thesis indicate that N1-suppression may represent a potential biomarker for psychosis proneness while neuroinflammation may represent a biomarker for psychosis onset. The implications of these results with respect to developing prophylactic treatments for psychosis transition are discussed.

Synchronized oscillations of ensembles of neurons in the brain underlie human cognition and behaviors. Neuronal network oscillations can be described by the physics of coupled dynamical systems. This dissertation examines the dynamic network activities in two distinct neurocognitive networks, the salience network (SN) and the ventral temporal cortex-dorsolateral prefrontal cortex (VTC-DLPFC) network, during perceptual decision-making (PDM). The key nodes of the SN include the right anterior insula (rAI), left anterior insula (lAI), and dorsal anterior cingulate cortex (dACC) in the brain. When and how a sensory signal enters and organizes within the SN before reaching the central executive network including the prefrontal cortex has been a mystery. Second, prior studies also report that perception of visual objects (face and house) involves a network of the VTC—the fusiform face area (FFA) and para-hippocampal place area (PPA)—and the DLPFC. How sensory information enters and organizes within the VTC-DLPFC network is not well understood, in milliseconds time-scale of human’s perception and decision-making. We used clear and noisy face/house image categorization tasks and scalp electroencephalography (EEG) recordings to study the dynamics of these networks. We demonstrated that beta (13–30 Hz) oscillation bound the SN, became most active around 100 ms after the stimulus onset, the rAI acted as a main outflow hub within the SN, and the SN activities were negatively correlated with the difficult tasks. We also uncovered that the VTC-DLPFC network activities were mediated by beta (13-30 Hz) and gamma (30-100 Hz) oscillations. Beta activities were enhanced in the time frame 125-250 ms after stimulus onset, the VTC acted as main outflow hub, and network activities were negatively correlated with the difficult tasks. In contrast, gamma activities were elevated in the time frame 0-125 ms, the DLPFC acted as a main outflow hub, and network activities—specifically the FFA-PPA pair—were positively correlated with the difficult tasks. These findings significantly enhance our understanding of how sensory information enters and organizes within the SN and the VTC-DLPFC network, respectively in PDM.

In this dissertation, I will discuss my research on the effect of spatial and feature attention effects on human electrophysiological responses to visual stimuli. This work involved minimizing previous sensory confounds in the physical structure in attention tasks, as well as separately analyzing how the brain responds to an orienting cue and a target that requires an overt behavioral response. By controlling for previous confounds, I was able to examine the effect of attention on sensory-evoked components in the absence of a sensory account for brain modulation.

Perceptual processing in the somatosensory system is crucial for humans and animals alike to perceive and interact with their surroundings. As with all cortical systems, information in the somatosensory pathway is processed by populations of densely interconnected neurons that display complex collective dynamics. This thesis is an exploration of neuronal population dynamics during somatosensory processing in humans. We present five inter-related studies into the encoding and processing of tactile vibrations of different frequencies in simple perceptual tasks, with the aim of uncovering how populations of neurons represent relevant tactile information and support perceptual and cognitive operations. Neuronal population dynamics are examined in two regimes: stimulus-driven dynamics and encoding, memory and decision dynamics. Each examination comprises one experimental and one theoretical study. In the stimulus-driven regime, we study scalp electroencephalography (EEG) and show n:m synchronization between cortical oscillations and periodic tactile stimuli. This indicates that neuronal populations in cortical somatosensory areas behave collectively as a nonlinear oscillator. Subsequent analysis of a model thalamocortical network with periodic forcing reveals that n:m synchronization at the population level emerges from an interaction between intrinsic neuron dynamics, network connections and the periodic input. In the memory intervals of a frequency discrimination task, we show specific patterns of cortical activity measured with EEG that correlate with both the recently encoded stimulus frequency and the distribution of frequencies across the entire stimulus set. This suggests both specific and general features of the stimulus are encoded and retained during this task. Finally, a neuronal population model of tactile frequency discrimination is presented, outlining a candidate mechanism by which cortical networks compare sequentially presented vibrations, and indicating a role for specific and general stimulus information in perceptual discrimination. We hence conclude that populations of neurons in somatosensory areas display complex patterns of activity in response to tactile vibrations in perceptual tasks. Collective dynamic activity in neuronal populations reflects the intrinsic properties of the cortical ensemble as well as multiple levels of information regarding the tactile stimulus and can support perceptual discrimination and other sensory processing.

Physiological signals such as Electroencephalography (EEG), Electrocardiography (ECG) and nonphysiological signals such as steering wheel related parameters have been investigated for drowsiness detection in previous researches. EEG has been deemed as a reliable way to detect drowsiness; while the accuracy of using ECG or steering wheel related parameters for drowsiness detection is not as high as that of EEG’s but they have the advantages of low cost and non-intrusive. This work is devoted to find out the possibility to enhance the accuracy of drowsiness detection based on the ECG and steering wheel related parameters. The correlation between EEG and ECG parameters and the correlation between EEG and steering wheel parameters for drowsiness detection are analyzed. If strong correlation between them are found it is possible to use the ECG and steering wheel related parameters to represent the EEG parameters which means the accuracy of drowsiness detection based on ECG and steering wheel parameters can be improved. Several parameters were chosen for the correlation analysis, the EEG parameters are the alpha, beta and theta band power, the ECG parameters are heart rate, Heart Rate Variability (HRV) parameters and a parameter derived from Detrended Fluctuation Analysis (DFA), and the steering wheel related parameters are four variables derived from steering wheel movement, all of these parameters are most commonly used parameters for driver drowsiness detection in the literature. The results of the analysis showed that neither the ECG parameters nor the steering wheel related parameters have strong correlation with EEG parameters. Parameters composed of combination of ECG signal parameters and steering wheel related parameters also did not show strong linear correlation with EEG parameters, however close nonlinear relationships have been found by artificial neural network methods, the promising results have largely increased the possibilities to build driver drowsiness detection system inexpensively and intrusively.