A low-cost circuit board design is presented, which in one embodiment is smaller than a credit card, for biopotential (EMG, ECG, or EEG) data acquisition, with a focus on EEG for brain-computer interface applications. The device combines signal conditioning, low-noise and high-resolution analog-to-digital conversion of biopotentials, user motion detection via accelerometer and gyroscope, user-programmable digital pre-processing, and data transmission via Bluetooth communications. The full development of the device to date is presented, spanning three embodiments. The device is presented both as a functional data acquisition system and as a template for further development based on its publicly-available schematics and computer-aided design (CAD) files. The design will be made available at the GitHub repository https://github.com/kellygs/eeg.

This study aims to explore whether human intentions to move or cease to move right and left hands can be decoded from spatiotemporal features in non-invasive electroencephalography (EEG) in order to control a discrete two-dimensional cursor movement for a potential multi-dimensional Brain-Computer interface (BCI). Five naïve subjects performed either sustaining or stopping a motor task with time locking to a predefined time window by using motor execution with physical movement or motor imagery. Spatial filtering, temporal filtering, feature selection and classification methods were explored. The performance of the proposed BCI was evaluated by both offline classification and online two-dimensional cursor control. Event-related desynchronization (ERD) and post-movement event-related synchronization (ERS) were observed on the contralateral hemisphere to the hand moved for both motor execution and motor imagery. Feature analysis showed that EEG beta band activity in the contralateral hemisphere over the motor cortex provided the best detection of either sustained or ceased movement of the right or left hand. The offline classification of four motor tasks (sustain or cease to move right or left hand) provided 10-fold cross-validation accuracy as high as 88% for motor execution and 73% for motor imagery. The subjects participating in experiments with physical movement were able to complete the online game with motor execution at the average accuracy of 85.5±4.65%; Subjects participating in motor imagery study also completed the game successfully. The proposed BCI provides a new practical multi-dimensional method by noninvasive EEG signal associated with human natural behavior, which does not need long-term training.

This research has developed a novel method which uses an easy to deploy single dry electrode wireless electroencephalogram (EEG) collection device as an input to an automated system that measures indicators of a participant’s attentiveness while they are watching a short training video. The results are promising, including 85% or better accuracy in identifying whether a participant is watching a segment of video from a boring scene or lecture, versus a segment of video from an attentiveness inducing active lesson or memory quiz. In addition, the final system produces an ensemble average of attentiveness across many participants, pinpointing areas in the training videos that induce peak attentiveness. Qualitative analysis of the results of this research is also very promising. The system produces attentiveness graphs for individual participants and these triangulate well with the thoughts and feelings those participants had during different parts of the videos, as described in their own words. As distance learning and computer based training become more popular, it is of great interest to measure if students are attentive to recorded lessons and short training videos. This research was motivated by this interest, as well as recent advances in electronic and computer engineering’s use of biometric signal analysis for the detection of affective (emotional) response. Signal processing of EEG has proven useful in measuring alertness, emotional state, and even towards very specific applications such as whether or not participants will recall television commercials days after they have seen them. This research extended these advances by creating an automated system which measures attentiveness towards short training videos. The bulk of the research was focused on electrical and computer engineering, specifically the optimization of signal processing algorithms for this particular application. A review of existing methods of EEG signal processing and feature extraction methods shows that there is a common subdivision of the steps that are used in different EEG applications. These steps include hardware sensing filtering and digitizing, noise removal, chopping the continuous EEG data into windows for processing, normalization, transformation to extract frequency or scale information, treatment of phase or shift information, and additional post-transformation noise reduction techniques. A large degree of variation exists in most of these steps within the currently documented state of the art. This research connected these varied methods into a single holistic model that allows for comparison and selection of optimal algorithms for this application. The research described herein provided for such a structured and orderly comparison of individual signal analysis and feature extraction methods. This study created a concise algorithmic approach in examining all the aforementioned steps. In doing so, the study provided the framework for a systematic approach which followed a rigorous participant cross validation so that options could be tested, compared and optimized. Novel signal analysis methods were also developed, using new techniques to choose parameters, which greatly improved performance. The research also utilizes machine learning to automatically categorize extracted features into measures of attentiveness. The research improved existing machine learning with novel methods, including a method of using per-participant baselines with kNN machine learning. This provided an optimal solution to extend current EEG signal analysis methods that were used in other applications, and refined them for use in the measurement of attentiveness towards short training videos. These algorithms are proven to be best via selection of optimal signal analysis and optimal machine learning steps identified through both n-fold and participant cross validation. The creation of this new system which uses signal processing of EEG for the detection of attentiveness towards short training videos has created a significant advance in the field of attentiveness measuring towards short training videos.

Epilepsy is a neurological disease causing seizures in its victims and affects approximately 50 million people worldwide. Successful treatment is dependent upon correct identification of the origin of the seizures within the brain. To achieve this, electroencephalograms (EEGs) are used to measure a patient’s brainwaves. This EEG data must be manually analyzed to identify interictal spikes that emanate from the afflicted region of the brain. This process can take a neurologist more than a week and a half per patient. This thesis presents a method to extract and process the interictal spikes in a patient, and use them to reduce the amount of data for a neurologist to manually analyze. The effectiveness of multiple neural network implementations is compared, and a data reduction of 3-4 orders of magnitude, or upwards of 99%, is achieved.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Title: The usage of qi-gong for training of perceiving body schema Summary: The goal of the work is to prove the presence of alpha activity in the electroencencefalographic record throughout the duration of the exercise qi-gong with open eyes and closed eyes, and to evaluate changes in the distribution of the scalp alpha activity with native EEG before and after the exercise. The observed research file was created from five probands between the ages of twenty-seven to fifty-two, which all practiced qi-gong for a duration of at least twelve months. The results showed the presence of alpha activity during exercising qi-gong with closed eyes in four probands, and in three probands there was also a present alpha activity during the exercise of qi-gong with open eyes. Furthermore was proven that the change in distribution of alpha activity during exercise of qi-gong with open eyes was from parietooccipital regions going temporo-frontally in comparison with the exercise of qi- gong with closed eyes and native EEG before and after exercise. The acquired results support in literature the described change of generators of alpha activity localized in the deeper structures of the brain. This process is connected with the decreased activity of the cerebral cortex with an increase in the particular limbic structures....

Title: Electroencephalographic correlates of prolonged locomotor performance of professional musicians Summary: The aim of this work is to verify the presence of alpha activity in the electroencephalographic recording during prolonged (20 minute) violin play and compare its morphological and topical parameters with the native EEG record before and after the performance. Research sample consisted of five professional violinist in the age range of 25- 60 years. The results showed the occurrence of alpha activity for four of five probands, in one case with a very low incidence. There has been also demonstrated changes in the distribution of alpha activity from parietooccipital areas before the preformance to central areas during the play and immediately after finishing. All probands showed increased amplitude of the alpha activity immediately after finishing. The obtained results confirm the changes of morphology and the changes of topic alpha activity during cognitive activities and at the onset of central fatigue during physical aktivity described in literature. These changes were demonstrated by increasing the amplitude of alpha activity and the shift from parietooccipital areas to central areas. Keywords: EEG, alpha aktivity, violin performance, brain mapping

Title: Brain activity assessment using sLORETA during modulated and physical activity. Objectives: The aim of this thesis was to compare changes in the electrical activity of cortical and deep brain structures using sLORETA program between the resting state, active movement and passive observation of identical motion performed by the author of this thesis and the same one shown in the video. Methods: In this research participated 12 university students (8 women, 4 men). Age of subjects was between 23 and 25 years. The whole experiment consisted of five parts: 1. electroencephalography in supinated lying position with opened eyes, 2. watching a video, where the selected movement was performed by a woman, 3. watching a video, where this movement was performed by a man, 4. watching the author performing the same movement, 5. performing this movement by subjects themselves. Each of this parts lasted two minutes. The tested movement was 1. diagonal (flexion and extension pattern) of PNF method for right upper extremity. During the whole experiment was registered electric activity of the brain using a scalp EEG. Obtained EEG signal was afterwards exported to sLORETA program, which enabled us to see the collected data in 3D Talairach system and also to make a statistical assessment using a Student's...

<p>Humans make decisions in highly complex physical, economic and social environments. In order to adaptively choose, the human brain has to learn about- and attend to- sensory cues that provide information about the potential outcome of different courses of action. Here I present three event-related potential (ERP) studies, in which I evaluated the role of the interactions between attention and reward learning in economic decision-making. I focused my analyses on three ERP components (Chap. 1): (1) the N2pc, an early lateralized ERP response reflecting the lateralized focus of visual; (2) the feedback-related negativity (FRN), which reflects the process by which the brain extracts utility from feedback; and (3) the P300 (P3), which reflects the amount of attention devoted to feedback-processing. I found that learned stimulus-reward associations can influence the rapid allocation of attention (N2pc) towards outcome-predicting cues, and that differences in this attention allocation process are associated with individual differences in economic decision performance (Chap. 2). Such individual differences were also linked to differences in neural responses reflecting the amount of attention devoted to processing monetary outcomes (P3) (Chap. 3). Finally, the relative amount of attention devoted to processing rewards for oneself versus others (as reflected by the P3) predicted both charitable giving and self-reported engagement in real-life altruistic behaviors across individuals (Chap. 4). Overall, these findings indicate that attention and reward processing interact and can influence each other in the brain. Moreover, they indicate that individual differences in economic choice behavior are associated both with biases in the manner in which attention is drawn towards sensory cues that inform subsequent choices, and with biases in the way that attention is allocated to learn from the outcomes of recent choices.</p> / Dissertation