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

The Development of a Common Factors Based Virtual Reality Therapy System for Remote Psychotherapy Applications

Mental health disorders such as depression and anxiety affect one in five adults in the United States. According to the 2019 National Survey on Drug Use and Health (NSDUH), non-serious mental illnesses are found in 30.6% of young adults aged 18-25 years old and 25.3% of adults aged 26-49 years old. In 2020, the NSDUH found that only 44.8% of all adults living with non-serious mental illnesses sought treatment [1]. In 2020 and 2021 with the rise of the COVID-19 pandemic, 41.5% of US adults reported to have been struggling symptoms of an anxiety of depressive disorder [2]. With this added burden, the increase in social isolation during the pandemic, and unknown long term psychological effects of the past year and a half, the need for an effective remote psychotherapy treatment is even more evident. The objective of this research is to address the growing need for a remote psychotherapy solution that is both accessible for isolated patients and effective. One approach to therapeutic healing that is standard in counseling psychology is the use of psychotherapy based on common factors theory. This theory poses that there are several common factors that need to be addressed for healing to occur. This research focuses on two of the common factors that are most difficult to reproduce in remote psychotherapy: the therapeutic alliance and the therapeutic environment [3]–[5]. We hypothesize that the use of a virtual reality (VR) and neurofeedback based psychotherapy system specifically designed based on common factors theory will lead to better performance in the therapeutic alliance between therapists and patients and ultimately, better outcomes for remote psychotherapy patients. The following specific aims address this hypothesis:Specific Aim 1: Design and Develop a Common Factors Based Virtual Reality Therapy for Remote Psychotherapy Applications. A full common factors based VR psychotherapy system was developed using Unity3D, Autodesk Maya, and MATLAB. Key components of the design include three virtual environments designed based on key elements of restorative environments (Forest World, Log Cabin, and Freud Therapist Office), two therapist avatars based on Jungian archetypes for healing (Woman Healer, Sage), a neurofeedback system using electroencephalography (EEG), a therapist interface, and a patient interface. Success was measured based on the prototype’s ability to be a fully functional remote psychotherapy treatment, its adherence to restorative environments design elements, and its adherence to Jungian archetypes design elements. Specific Aim 2: Determine the functionality and usability of the novel common factors based VR therapy system for therapists. The first step to determining the efficacy of a novel treatment system in psychotherapy is to analyze the functionality and usability of the treatment for therapists. Specifically, this study examined if therapists are able to effectively use this system for the remote treatment of depression and general anxiety. A proof of concept study was conducted with 21 observing counselors in training to examine the functionality and usability of the VR enhanced therapy system for therapists. The session was conducted with a professional therapist and a patient using the VR system in another room. Measures from this study will include the 1. Client Reactions Systems, 2. Perceived Restorative Scale, 3. Session Evaluation Questionnaire, and 4. Presence Questionnaire. Success will be determined by examining the neutral score for each these metrics, and comparing the scores received by therapists to the average. The treatment was considered successful if the novel VR treatments preforms as well or better than the average across all metrics. Specific Aim 3: Determine the functionality and usability of the novel common factors based VR therapy system for patients. A proof of concept study was conducted to determine if the novel VR enhanced therapy is as good or better at reducing symptoms of depression and anxiety in patients in comparison to existing CBT effects and to determine whether the therapeutic alliance is enhanced in VR therapy. The study examined examine 30 adults in Lawrence, Kansas and the surrounding areas with counselors playing the role as therapists in a solution focused counseling session. The patients were split into two groups: a control Zoom video chat based remote therapy session and the novel VR based therapy session. Measures from this study will include the 1. Client Reactions Systems, 2. Perceived Restorative Scale, 3. Session Evaluation Questionnaire, and 4. Presence Questionnaire. The treatment was considered successful if the novel VR treatments preforms as well or better than the control across all metrics. Future Development and Research: Beyond this dissertation work, we plan to continue to develop more environments and avatars, determine relative efficacy of the therapy system through further human subjects’ studies, and explore its use in the treatment of other populations including the military and other conditions including post-traumatic stress disorder (PTSD). At this moment, we are also pursuing opportunities for the commercialization of this work including obtaining a provisional patent and submitting a full international patent, exploring a licensing agreement with a company, and examining the viability of starting a new venture in the field

Investigation of bus passenger discomfort and driver fatigue: An electroencephalography (EEG) approach

Efforts to improve urban bus transport systems’ comfort and increase user satisfaction have been made for many years across the globe. Increasing bus users and reducing car users has an economic benefit. Whenever the urban bus share is larger than 25%, there are journey time savings due to lower congestion levels on the network. A driver’s loss of alertness due to fatigue has been recognised to be one of the major factors responsible for road accidents/crashes for many decades. Comfort and fatigue are psychophysiological phenomena. Objective measures of human psychological and physiological factors must be defined, investigated, and evaluated in order to have an indepth understanding of the cause-effect mechanisms regulating psychophysiological factors. Electroencephalography (EEG) developed as bio-sensor equipment to interpret and collate bioelectrical signals was used to gather the time-series quantitative data of urban bus passengers and HGV drivers. This study’s EEG data application was designed to link the brain activity dynamics to dynamic experimental design variables or tasks by correlating increased or decreased measured brain activity by using a baseline for comparisons. Two experiments were conducted in this study. The first sought to understand the influence of driving time and rest breaks on a driver’s psychophysiological response. Therefore, the EEG data was collected, categorised and grouped based on two hours of driving before a 30 minute break, two hours of driving after a 30 minute break and four hours of driving with no break. The Samn-Perelli seven-point scale of fatigue assessment was used to evaluate the influence of the duration of driving time on a driver’s performance decrements. The second experiment investigated bus passenger discomfort by examining experimental design stage-related changes in EEG measured by using a control experiment for comparison. Consequently, datasets in two stages were collected for each subject (passenger), including the stationary laboratory (control) and dynamic onboard bus environment experiments. A subjective evaluation of the average ride comfort on each stage of the experiments was conducted by using the recommended assessment scale of the International Standard ISO 2631-1. The ERP EEG oscillations were evaluated by decomposing the EEG signals into magnitudes and phase information, and then characterising their changes relative to the experimentally designed phases and variables. A two-way analysis of variance (ANOVA) was conducted to test the model’s predictor under different experimental conditions for passenger discomfort and driving fatigue experiments. Efforts to improve urban bus transport systems’ comfort and increase user satisfaction have been made for many years across the globe. Increasing bus users and reducing car users has an economic benefit. Whenever the urban bus share is larger than 25%, there are journey time savings due to lower congestion levels on the network. A driver’s loss of alertness due to fatigue has been recognised to be one of the major factors responsible for road accidents/crashes for many decades. Comfort and fatigue are psychophysiological phenomena. Objective measures of human psychological and physiological factors must be defined, investigated, and evaluated in order to have an indepth understanding of the cause-effect mechanisms regulating psychophysiological factors. Electroencephalography (EEG) developed as bio-sensor equipment to interpret and collate bioelectrical signals was used to gather the time-series quantitative data of urban bus passengers and HGV drivers. This study’s EEG data application was designed to link the brain activity dynamics to dynamic experimental design variables or tasks by correlating increased or decreased measured brain activity by using a baseline for comparisons. Two experiments were conducted in this study. The first sought to understand the influence of driving time and rest breaks on a driver’s psychophysiological response. Therefore, the EEG data was collected, categorised and grouped based on two hours of driving before a 30 minute break, two hours of driving after a 30 minute break and four hours of driving with no break. The Samn-Perelli seven-point scale of fatigue assessment was used to evaluate the influence of the duration of driving time on a driver’s performance decrements. The second experiment investigated bus passenger discomfort by examining experimental design stage-related changes in EEG measured by using a control experiment for comparison. Consequently, datasets in two stages were collected for each subject (passenger), including the stationary laboratory (control) and dynamic onboard bus environment experiments. A subjective evaluation of the average ride comfort on each stage of the experiments was conducted by using the recommended assessment scale of the International Standard ISO 2631-1. The ERP EEG oscillations were evaluated by decomposing the EEG signals into magnitudes and phase information, and then characterising their changes relative to the experimentally designed phases and variables. A two-way analysis of variance (ANOVA) was conducted to test the model’s predictor under different experimental conditions for passenger discomfort and driving fatigue experiments.The variability in the driver’s psychophysiological responses to the duration of driving occurs systematically. The effects appear to be progressive and aligned such that the driving performance was worst during the last 60 minutes of driving for four hours without a break, but better during the first 30 minutes. Data analysis also showed that a pronounced psychophysiological response exists relative to the influence of the road roughness characteristics, the passenger’s postures, and the bus type. Further analysis of passenger discomfort showed that passengers are more strained while in a standing posture than in a seated posture, irrespective of the bus type and the degree of the road’s roughness. The results indicated that passenger comfort deteriorates as the road roughness coefficient increases. Furthermore, the results demonstrated that female passengers express more discomfort/dissatisfaction than males under the same experimental conditions. Therefore, female passengers are more sensitive than males to a deviation from optimal comfort conditions.This study provides opportunities for future research applications of EEG in transport research studies. It also provides a platform for evaluating different Intelligent Transport System (ITS) technologies, particularly passenger’s reactions in autonomous vehicles