A game player expertise level classification system using electroencephalography (EEG)

The success and wider adaptability of smart phones has given a new dimension to the gaming industry. Due to the wide spectrum of video games, the success of a particular game depends on how efficiently it is able to capture the end users' attention. This leads to the need to analyse the cognitive aspects of the end user, that is the game player, during game play. A direct window to see how an end user responds to a stimuli is to look at their brain activity. In this study, electroencephalography (EEG) is used to record human brain activity during game play. A commercially available EEG headset is used for this purpose giving fourteen channels of recorded EEG brain activity. The aim is to classify a player as expert or novice using the brain activity as the player indulges in the game play. Three different machine learning classifiers have been used to train and test the system. Among the classifiers, naive Bayes has outperformed others with an accuracy of 88%, when data from all fourteen EEG channels are used. Furthermore, the activity observed on electrodes is statistically analysed and mapped for brain visualizations. The analysis has shown that out of the available fourteen channels, only four channels in the frontal and occipital brain regions show significant activity. Features of these four channels are then used, and the performance parameters of the four-channel classification are compared to the results of the fourteen-channel classification. It has been observed that support vector machine and the naive Bayes give good classification accuracy and processing time, well suited for real-time applications

Physiological Approach To Characterize Drowsiness In Simulated Flight Operations During Window Of Circadian Low

Drowsiness is a psycho-physiological transition from awake towards falling sleep and its detection is crucial in aviation industries. It is a common cause for pilot’s error due to unpredictable work hours, longer flight periods, circadian disruption, and insufficient sleep. The pilots’ are prone towards higher level of drowsiness during window of circadian low (2:00 am- 6:00 am). Airplanes require complex operations and lack of alertness increases accidents. Aviation accidents are much disastrous and early drowsiness detection helps to reduce such accidents. This thesis studied physiological signals during drowsiness from 18 commercially-rated pilots in flight simulator. The major aim of the study was to observe the feasibility of physiological signals to predict drowsiness. In chapter 3, the spectral behavior of electroencephalogram (EEG) was studied via power spectral density and coherence. The delta power reduced and alpha power increased significantly (

Mental Workload Assessment using Low-Channel Prefrontal EEG Signals

Objective: Monitoring stress using physiological signals has recently achieved a lot of attention since it has a significant adverse influence on an individual daily's health and efficiency. As it has been proven that stress and mental workload are proportionally correlated, several studies have proposed algorithms for stress monitoring by increasing the mental workload. Despite the promising results reported in the literature, a majority of the proposed algorithms require the employment of several physiological signals which hinder their real-life application. Nonetheless, the advent of low-cost wearable devices has provided a new possibility for outdoor stress monitoring. The objective of this paper is to present an algorithm for stress detection using low-channel prefrontal electroencephalography (EEG) data. Methods: Firstly, artifacts in EEG signals are removed. Secondly, EEG signals are split into sub-bands using the discrete wavelet transform and two nonlinear parameter-free features are extracted. Thirdly, the extracted features are fed to three classifiers, i.e., support vector machine, Adaboost, and the K-Nearest Neighbours to discriminate stress from relaxed states. Main results: According to the obtained results, the highest accuracy (80.24%) was achieved using the AdaBoost classifier. Significance:Given that the proposed method does not require any parameter adjustment before processing, it has the potential to be used in real-world scenarios.Peer reviewe

Open Source EEG Platform with Reconfigurable Features for Multiple-Scenarios

Electroencephalogram (EEG) acquisition systems are widely used as diagnostic and research tools. This document shows the implementation of a reconfigurable family of three affordable 8-channels, 24 bits of resolution, EEG acquisition systems intended for a wide variety of research purposes. The three devices offer a modular design and upgradability, permitting changes in the firmware and software. Due to the nature of the Analog Front-End (AFE) used, no high-pass analog filters were implemented, allowing the capture of very low frequency components. Two systems of the family, called “RF-Brain” and “Bluetooth-Brain”, were designed to be light and wireless, planned for experimentation where movement of the subject cannot be restricted. The sample rate in these systems can be configured up to 2000 samples per second (SPS) for the RF-Brain and 250 SPS for the Bluetooth-Brain when the 8 channels are used. If fewer channels are required, the sampling frequency can be higher (up to 4 kSPS or 2 kSPS for 1 channel for RF-Brain and Bluetooth-Brain respectively). The third system, named “USB-Brain”, is a wired device designed for purposes requiring high sampling frequency acquisition and general purpose ports, with sampling rates up to 4 kSPS

Automated testsystem of COGNISION headset for cognitive diagnosis.

There are more than 15 million Americans suffering from a chronic cognitive disability in the Unites States. Researchers have been exploring many different quantitative measures, such as event related potentials (ERP), electro-encephalogram (EEG), Magnetic Encephalogram (MEG) and Brain volumetry to accurately and repeatedly diagnose patients suffering from debilitating cognitive disorders. More than a million cases have been diagnosed every year, with many of those patients being misdiagnosed as a result of inadequate diagnostic and quality control tools. As a result, the medical device industry has been actively developing alternative diagnostic techniques, which implement one or more quantitative measures to improve diagnosis. For example, Neuronetrix (Louisville, KY) developed COGNISION™ that utilizes both ERP and EEG data to diagnose the cognitive ability of patients. The system has shown to be a powerful tool; however, its commercial success would be limited without lack of a fast and effective method of testing and validating the product. Thus, the goal of this study is to develop, test and validate a new “Testset” system for accurately and repeatedly validating the COGNISION™ Headset. A Testset was constructed that is comprised of a software control component designed using the Labview G programming language, which runs on a computer terminal, a Data Acquisition (DAQ) card and switching board. The Testset is connected to a series of testing fixtures for interfacing with the various components of the Headset. The Testset evaluates the Headset at multiple stages of the manufacturing process as a whole system or by its individual components. At the first stage of production the Electrode Strings, amplifier board (Uberyoke), and Headset Control Unit (HCU) are tested and operated as individual printed circuit boards (PCBs). These components are again tested as mid-level assemblies and/or at the finished product stage as a complete autonomous system with the Testset monitoring the process. All tests are automated, requiring only a few parameters to be defined before a test is initiated by a single button press, and then selected test sequences are begun for that particular component or system and are completed in a few minutes. A total of 2 Testsets were constructed and used to validate 10 Headsets. An automated software system was designed to control the Testset. The Testset demonstrated the ability to validate and test 100% of the individual components and completed assembled Headsets. The Testsets were found to be within 5% of the manufacturing specifications. Subsequently, the Automated Testset developed in this study enabled the manufacturer to provide a comprehensive report on the calibration parameters of the Headset, which is retained on file for each unit sold. The automated testsystem’s statistical analysis shows that the two Testsets yielded reliable and consistent results with each other

Effects of Sensorimotor Perturbations on Balance Performance and Electrocortical Dynamics

Humans must frequently adapt their posture to prevent loss of balance. Such balance control requires complex, precisely-timed coordination among sensory input, neural processing, and motor output. Despite its importance, our current understanding of cortical involvement during balance control remains limited by traditional neuroimaging methods, which are stationary and have poor time resolution. High-density electroencephalography (EEG), combined with independent component analysis, has become a promising tool for recording cortical dynamics during balance perturbations due to its portability and high temporal resolution. Additionally, recent improvements in immersive virtual reality headsets may provide new rehabilitative paradigms, but the effects of virtual reality on balance and cortical function remain poorly understood. In my first study, I recorded high-density EEG from healthy, young adult subjects as they walked along a beam with and without virtual reality high heights exposure. While virtual high heights did induce stress, the use of virtual reality during the task increased performance errors and EEG measures of cognitive loading compared to real-world viewing without a headset. In my second study, I collected high-density EEG from healthy young adults as they walked along a treadmill-mounted balance beam to determine the effect of a transient visual perturbation on training in virtual reality. Subjects in the perturbations group improved comparably to those that trained without virtual reality, indicating that the perturbation helped subjects overcome the negative effects of virtual reality on motor learning. The perturbation primarily elicited a cognitive change. In my third study, healthy, young adult EEG was recorded during physical pull and visual rotation perturbations to tandem walking and tandem standing. I found similar electrocortical patterns for both perturbation types, but different cortical areas were involved for each. In my fourth study, I used a phantom head to validate EEG connectivity methods based on Granger causality in a real-world environment. In general, connectivity measures could determine the underlying connections, but many were susceptible to high-frequency false positives. Using data from my third study, my fifth study analyzed corticomuscular connectivity patterns following sensorimotor balance perturbations. I found strong occipito-parietal connections regardless of perturbation type, along with evidence of direct muscular control from the supplementary motor area during the standing perturbation response. Taken together, the work presented in this dissertation greatly expands upon the current knowledge of cortical processing during sensorimotor balance perturbations and the effect of such perturbations on short-term motor learning, providing multiple avenues for future exploration.PHDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147615/1/stepeter_1.pd

Development of Smart Security System for Building or Laboratory Entrance based on human’s brain (EEG) and Voice Signals

The drastic increment in cyber-crimes and violent attacks involving our properties and lives made the world become much vigilant towards ill-intentioned peoples. Thus, it leads to the booming of smart security system industry which relies heavily on biometrics technology. However, due to certain circumstances, some users may find the existing biometrics technologies such as fingerprint, palm, iris and face recognition are unable to detect the necessary data precisely due to the physical injuries of the users. Furthermore, the fact that these biometrics technologies are easily retrieved from the user and be used as counterfeit to access to the security system undetected. Thus, in this research, in order to enhance the existing security system based on the biometric technologies, the combination of the human physiological signals such as brain and voice signals will be employed in order to unlock the magnetic door entrance to the laboratory, building or office. This research has utilized mobile Electroencephalogram (EEG) headset and voice recognizer to capture human’s brain and voice signals respectively. The extracted features from the captured signals then are analyzed, classified and translated to determine the device command for the microcontroller to control the door entrance’s locking system. The high rate of classification results of the selected features of EEG and voice signals at 96.7% and 99.3% respectively show that selected features can be translated to command parameters to control device

Southwest Research Institute assistance to NASA in biomedical areas of the technology utilization program

The activities are reported of the NASA Biomedical Applications Team at Southwest Research Institute between 25 August, 1972 and 15 November, 1973. The program background and methodology are discussed along with the technology applications, and biomedical community impacts