John JB Allen

PMID: 12765856;Abstract:

Perception of illness has been described as an important predictor in the medical health psychology literature, but has been given little attention in the domain of mental disorders. The patient's Perception of Depression Questionnaire (PDIQ) is a newly developed measure whose factor structure and psychometric properties were evaluated on a sample of 174 outpatients meeting criteria for major depressive disorder. The clinical utility of the questionnaire was assessed on a sub-sample of 121 participants in a study of acupuncture treatment for depression. The questionnaire has four subscales, each with high internal consistency and high test-retest reliability. These four subscales are: Self-Efficacy, which reflects perceived controllability of the illness, Externalizing, which reflects attributing the illness to external causes, Hopeless/Flawed, which reflect a belief that depression is a personal trait and therefore there is little hope for cure, and Holistic, which reflects a belief in alternative therapies. Although the PDIQ did not predict outcome, its subscales were related to adherence to treatment, treatment preference, expectations, and therapeutic alliance. The subscales have adequate convergent/discriminant validity and are clinically relevant to aspects of treatment provision. © 2003 Elsevier Science Ltd. All rights reserved.

Abstract:

Emotion is an important aspect in the interaction between humans. It is fundamental to human experience and rational decision-making. There is a great interest for detecting emotions automatically. A number of techniques have been employed for this purpose using channels such as voice and facial expressions. However, these channels are not very accurate because they can be affected by users' intentions. Other techniques use physiological signals along with electroencephalography (EEG) for emotion detection. However, these approaches are not very practical for real time applications because they either ask the participants to reduce any motion and facial muscle movement or reject EEG data contaminated with artifacts. In this paper, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature selection mechanism to extract features that are relevant to the emotion detection task based on neuroscience findings. We reached an average accuracy of 51% for joy emotion, 53% for anger, 58% for fear and 61% for sadness. © 2010 ACM.

This paper reports 2 experiments, one of which was part of Logan Trujillo's dissertation, conducted when he was a graduate student jointly working in my lab and John Allen's lab. ;Collaborative with graduate student: Yes;Collaborative with faculty member in unit: Yes;Full Citation: Trujillo, L. T., Allen, J. J. B., & Peterson, M. A. (under revision). Neurophysiological evidence for differential processing of high- and low- competition figure-ground stimuli. ;Status: Under Revision (Revise and Resubmit);

Abstract:

Emotion is an important aspect in the interaction between humans. There is a great interest for detecting emotions automatically. Current approaches for emotion detection using EEG are not practical for real-life situations because researchers ask participants to reduce any motion and facial muscle movement, reject noisy EEG data and rely on large number of electrodes. In this paper, we propose an approach that analyses highly contaminated brain signals. We then extract relevant features for the emotion detection task based on neuroscience findings. We reached an average accuracy of 51%, 53%, 58% and 61% for joy, anger, fear and sadness, respectively. We are also applying our approach on fewer number of electrodes that ranges from 4 to 25 electrodes and we reached an average classification accuracy of 33% for joy emotion, 38% for anger, 33% for fear and 37.5% for sadness using 4 or 6 electrodes only. Copyright © 2013 Inderscience Enterprises Ltd.

PMID: 15032992;Abstract:

Although resting frontal electroencephalographic (EEG) alpha asymmetry has been shown to be a stable measure over time in nonclinical populations, its reliability and stability in clinically depressed individuals has not been fully investigated. The internal consistency and test-retest stability of resting EEG alpha (8-13 Hz) asymmetry were examined in 30 women diagnosed with major depression at 4-week intervals for 8 or 16 weeks. Asymmetry scores generally displayed good internal consistency and exhibited modest stability over the 8- and 16-week assessment intervals. Changes in asymmetry scores over this interval were not significantly related to changes in clinical state. These findings suggest that resting EEG alpha asymmetry can be reliably assessed in clinically depressed populations. Furthermore, intraclass correlation stability estimates suggest that although some traitlike aspects of alpha asymmetry exist in depressed individuals, there is also evidence of changes in asymmetry across assessment occasions that are not closely linked to changes in depressive severity.