Parameter tuning for enhancing inter-subject emotion classification in four classes for vr-eeg predictive analytics

The following research describes the potential in classifying emotions using wearable EEG headset while using a virtual environment to stimulate the responses of the users. Current developments on emotion classification have always steered towards the use of a clinical-grade EEG headset with a 2D monitor screen for stimuli evocations which may introduce additional artifacts or inaccurate readings into the dataset due to users unable to provide their full attention from the given stimuli even though the stimuli presentated should have been advantageous in provoking emotional reactions. Furthermore, the clinical-grade EEG headset requires a lengthy duration to setup and avoiding any hindrance such as hairs hindering the electrodes from collecting the brainwave signals or electrodes coming loose thus requiring additional time to work to fix the issue. With the lengthy duration of setting up the EEG headset, the user may expereince fatigue and become incapable of responding naturally to the emotion being presented from the stimuli. Therefore, this research introduces the use of a wearable low-cost EEG headset with dry electrodes that requires only a trivial amount of time to set up and a Virtual Reality (VR) headset for the presentation of the emotional stimuli in an immersive VR environment which is paired with earphones to provide the full immersive experience needed for the evocation of the emotion. The 360 video stimuli are designed and stitched together according to the arousal-valence space (AVS) model with each quadrant having an 80-second stimuli presentation period followed by a 10-second rest period in between quadrants. The EEG dataset is then collected through the use of a wearable low-cost EEG using four channels located at TP9, TP10, AF7, AF8. The collected dataset is then fed into the machine learning algorithms, namely KNN, SVM and Deep Learning with the dataset focused on inter-subject test approaches using 10-fold cross-validation. The results obtained found that SVM using Radial Basis Function Kernel 1 achieved the highest accuracy at 85.01%. This suggests that the use of a wearable low-cost EEG headset with a significantly lower resolution signal compared to clinical-grade equipment which utilizes only a very limited number of electrodes appears to be highly promising as an emotion classification BCI tool and may thus spur up open up myriad practical, affordable and cost-friendly solutions in applying to the medical, education, military, and entertainment domains

In the mood: online mood profiling, mood response clusters, and mood-performance relationships in high-risk vocations

The relationship between mood and performance has long attracted the attention of researchers. Typically, research on the mood construct has had a strong focus on psychometric tests that assess transient emotions (e.g., Profile of Mood States [POMS]; McNair, Lorr, & Dropplemann, 1971, 1992; Terry, Lane, Lane, & Keohane, 1999). Commonly referred to as mood profiling, many inventories have originated using limited normative data (Terry et al., 1999), and cannot be generalised beyond the original population of interest. With brevity being an important factor when assessing mood, Terry et al. (1999) developed a 24-item version of the POMS, now known as the Brunel Mood Scale (BRUMS). Including six subscales (i.e., tension, depression, anger, vigour, fatigue, and confusion), the BRUMS has undergone rigorous validity testing (Terry, Lane, & Fogarty, 2003) making it an appropriate measure in several performance environments. Mood profiling is used extensively for diverse purposes around the world, although Internet-delivered interventions have only recently been made available, being in conjunction with the proliferation of the World Wide Web. Developed by Lim and Terry in 2011, the In The Mood website (http://www.moodprofiling.com) is a web-based mood profiling measure based on the BRUMS and guided by the mood-performance conceptual framework of Lane and Terry (2000). The focus of the website is to facilitate a prompt calculation and interpretation of individual responses to a brief mood scale, and link idiosyncratic feeling states to specific mood regulation strategies with the aim of facilitating improved performance. Although mood profiling has been a popular clinical technique since the 1970s, currently there are no published investigations of whether distinct mood profiles can be identified among the general population. Given this, the underlying aim of the present research was to investigate clusters of mood profiles. The mood responses (N = 2,364) from the In The Mood website were analysed using agglomerative, hierarchical cluster analysis which distinguished six distinct and theoretically meaningful profiles. K-means clustering with a prescribed six-cluster solution was used to further refine the final parameter solution. The mood profiles identified were termed the iceberg, inverse iceberg, inverse Everest, shark fin, surface, and submerged profiles. A multivariate analysis of variance (MANOVA) showed significant differences between clusters on each dimension of mood, and a series of chi-square tests of goodness-of-fit indicated that gender, age, and education were unequally distributed. Further, a simultaneous multiple discriminant function analysis (DFA) showed that cluster membership could be correctly classified with a high degree of accuracy. Following this, a second (N = 2,303) and third (N = 1,865) sample each replicated the results. Given that certain vocations are by nature riskier than others (Khanzode, Maiti, & Ray, 2011) highlighting the importance of performance in the workplace, the present research aimed to further generalise the BRUMS to high-risk industries using a web-based delivery method. Participants from the construction and mining industries were targeted, and the relationship between mood and performance in the context of safety was investigated, together with associated moderating variables (i.e., gender, age, education, occupation, roster, ethnicity, and location)

휴대용 EEG 장비를 이용한 학습자의 뇌파 분석 - 학습 양식 및 Raven 데이터를 중심으로

학위논문 (석사)-- 서울대학교 대학원 : 사범대학 수학교육과, 2018. 2. 조한혁.최근 증거 기반 교수(evidence based instruction), 데이터 기반 교수(data based instruction)에 대한 관심이 증가하고 있다. 데이터 기반 수학 교육을 위해서는 교수 학습 과정에서 학습자의 상태를 정확하게 진단하고 이에 맞추어 적절한 교수를 처방하는 것이 필요하다. 이를 위한 많은 교육 연구가 진행되고 있는 가운데 신경 과학은 여기에 새로운 가능성을 열어줄 수 있을 것으로 기대된다. 교육 연구에서 신경 과학과의 협력 연구는 하나의 흐름이 되어가고 있음에도 불구하고 아직 수학 교육 연구에서 뇌파 연구는 많지 않은 것이 현실이다. 이에 본 연구는 뇌파 연구의 기초 연구로서 휴대용 뇌파 장비와 파이썬을 이용하여 뇌파 측정 및 분석 시스템을 구축하고 이를 이용한 뇌파 분석이 가능한 지 탐구하였다. 나아가 휴대용 뇌파 장비로 측정된 데이터를 활용한 머신 러닝 적용 사례를 개발함으로써 이러한 뇌파 분석 활동이 교수 학습 과정에서 학습 내용으로 활용될 수 있는 가능성 또한 모색하였다. 본 연구는 수학교육 신경과학의 기초 연구로서 증거 기반적인 견해를 제공하며 수학 교수와 학습에 대한 보다 깊고 폭넓은 이해가 가능하도록 한다. 특히 휴대용 EEG 장비를 활용함으로써 EEG 연구가 교육 연구와 학교 현장에 확산될 수 있도록 하는데 기여한다는 측면에서 의의가 있다.I. 서론 1 1. 연구의 필요성 및 목적 1 2. 연구 문제 8 3. 용어의 정의 9 II. 이론적 배경 11 1. 수학교육 신경과학 11 2. 뇌파 16 2.1. 대뇌의 구조 16 2.2. 뇌파와 EEG 17 2.3. 국제 표준 10-20 시스템 18 2.4. 수학교육 EEG 선행연구 19 3. 휴대용(Portable) EEG 시스템 22 4. 유동지능과 패턴 추론 : Raven의 누진행렬검사 24 5. 학습 양식 : Kolb의 LSI 27 5.1. KLSI 3.1 27 6. 머신 러닝(machine learning, 기계학습) 30 6.1. K-means 클러스터링 32 6.2. 신경망(Neural Network) 33 III. 뇌파 측정 및 분석 환경 설계 38 1. 뇌파 측정 도구의 선정 38 2. 뇌파의 측정 41 3. 뇌파의 분석 43 4. 뇌파 측정 및 분석 시스템 구성 46 IV. 뇌파 측정 및 분석 사례 51 1. 연구 설계 및 방법 51 1.1. 연구 참여자 51 1.2. 연구 절차 52 1.3. 검사 도구 54 2. 뇌파와 APM 분석 결과 59 2.1. 뇌파와 APM 사이의 Spearman의 상관 분석 59 2.2. 뇌파와 APM 사이의 대응 표본 T검정 67 2.3. 뇌파와 Raven 총점 사이의 다중 회귀 분석 68 2.4. 뇌파와 난도 사이의 반복측정 분산분석 71 3. 뇌파와 KLSI 분석 결과 74 3.1. EO 상태 뇌파 분석 75 3.2. EC 상태 뇌파 분석 78 V. 머신러닝에의 응용 79 1. K-means clustering 79 2. 신경망 82 2.1. EO, EC 상태 신경망 82 2.2. 난도와 신경망 83 2.3. 정오답과 신경망 84 3. 논의 85 VI. 결론 및 제언 86 1. 결론 86 2. 제언 88 참고문헌 90 부록 98Maste

Aerospace medicine and biology: A cumulative index to the continuing bibliography of the 1973 issues

A cumulative index to the abstracts contained in Supplements 112 through 123 of Aerospace Medicine and Biology A Continuing Bibliography is presented. It includes three indexes: subject, personal author, and corporate source

Brain Waves, A Cultural History: Oscillations of Neuroscience, Technology, Telepathy, and Transcendence

This project proceeds from a narrow question: What, if anything, is a brain wave? Beguiling in its simplicity, this question prompts a cultural-historical investigation that spans over 150 years of science, technology, and society. Proposed in 1869, the original theory of brain waves cites etheric undulations to explain reports of apparent thought transference. Though most modern thinkers no longer believe in outright telepathy, I argue that dreams of thought transmission and other mental miracles subtly persist—not in obscure and occult circles, but at the forefront of technoscience. A hybrid of science and fiction, brain waves represent an ideal subject through which to explore the ways in which technical language shrouds spiritual dreams. Today, the phrase “brain waves” often function as shorthand for electrical changes in the brain, particularly in the context of technologies that purport to “read” some aspect of mental function, or to transmit neural data to a digital device. While such technologies appear uniquely modern, the history of brain waves reveals that they are merely the millennial incarnation of a much older hope—a hope for transmission and transcendence via the brain’s emanations

A fugal discourse on the electromagnetic coupling of electromagnetic processes in the earth-ionosphere and the human brain

There exists a space between the ionosphere and the surface of the earth within which electromagnetic standing waves, generated by lightning strikes, can resonate around the earth; these standing waves are known collectively as the Schumann resonances. In the late 1960s König and Ankermuller already reported striking similarities between these electromagnetic signals and those recorded from the electroencephalograms (EEG) of the human brain; both signals exhibit similar characteristics in terms of frequency and electric and magnetic field intensity. The analyses reported here demonstrate that 1) microscopic (brain) and macroscopic (earth) representations of natural electromagnetic fields are conserved spatially, 2) that electric fields recorded from human brains exhibit strong correlation with the strength of the these parameters and 3) that the human brain periodically synchronizes with signals generated within the earth-ionosphere waveguide at frequencies characteristic of the Schumann resonance for periods of about 300 msec. These findings recapitulate 17th century ideas of harmony amongst the cerebral and planetary spheres and may provide the means necessary to quantitatively investigate concepts of early 20th century psychologyDoctor of Philosophy (PhD) in Human Studie

URI Undergraduate and Graduate Course Catalog 2022-2023

This is a downloadable PDF version of the University of Rhode Island course catalog.https://digitalcommons.uri.edu/course-catalogs/1074/thumbnail.jp