Emo vs id
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This study used fMRI to compare neural activity during an emotional expression working memory task and a face identity working memory task. Behavioral results showed greater accuracy and faster responses on the identity task compared to the emotional task. Neuroimaging results revealed increased dorsolateral prefrontal cortex activity during the emotional task and increased lateral fusiform gyrus activity during the identity task, supporting the involvement of different brain regions for working memory of facial expressions versus identity.
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Stefan_Dukic
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This document describes an experiment that used EEG signals to detect mental stress in human subjects. EEG signals were collected from subjects using electrodes placed according to the 10-20 international system. Stress was induced using images from the IAPS dataset. Machine learning algorithms like ICA, DWT, and PCA were used to preprocess the signals, extract features, and reduce dimensions. SVM and neural networks were then used to classify states as stressed or calm, achieving accuracies of 82% and 80% respectively. The study aimed to determine a subject's mental state as stressed or not stressed, rather than determining causes or levels of stress.
Prediction Model for Emotion Recognition Using EEG
The document describes a study that compares different machine learning models for emotion recognition using EEG data. The study uses EEG data collected from patients with depression and healthy controls. It extracts features from the EEG data, including linear and nonlinear parameters from different frequency bands. It then uses classifiers like random forest, KNN, CNN, and LSTM to classify emotions as positive, negative or neutral. The random forest model achieved the best accuracy for identifying depression patients' emotions. The study provides a framework for EEG data collection, preprocessing, feature extraction and applying different machine learning models to optimize emotion recognition from EEG signals.
Bayesain Hypothesis of Selective Attention - Raw 2011 poster
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Emo vs id
- 2. a r t i c l e i n f o a b s t r a c t Article history: Received : 26 October 2010 Revised : 15 February 2011 Accepted : 17 February 2011 Available online : 22 February 2011 Keywords : Facial expressions Emotion Working
- 3. PURP OSE using the n-back task to compare neural responses during - an emotional expression WM task - a face identity WM task.
- 4. METH ODS - 20 undergraduates (9 female; 18–23 years old, mean age=19.2) normal no psychiatric medication reported no significant neurological no psychiatric history Participants - informed consent approved by Dartmouth College Committee for the Protection of Human Subjects. - 2 participants were removed
- 5. METH ODS - All tasks were performed while participants were in the scanner. Procedure - Pilot data 15 participants performed a 2-back 15 participants performed a 3-back task EMO ID mean accuracy difference=7% mean accuracy=70% (mean accuracy=77% mean accuracy difference=11%
- 6. METH ODS - Each participant had two runs of 16 practice trials Procedure 4 SHAPES First practice run Shape of the image second practice run color of the image four alternating blocks of EMO and ID trials.
- 7. using the n-back task to compare neural responses during - an emotional expression WM task - a face identity WM task. Selected images of four identities - 2 female The NimStim standardized facial expression METH ODSProcedure
- 8. four emotional expressions - for a total of 16 faces Randomly presented presented once per block for 2000 ms followed by a fixation cross that appeared for 500 ms METH ODSProcedure
- 10. METH ODS Behavioral data analysis As has been previously described (Wilcox, 1992; Bush et al., 1993), trimming is an effective technique for dealing with outliers. Therefore, we chose to trim trials for each subject according to a typical trimming threshold of the top 10% and bottom 10% reaction time (RT) values. http://www.youtube.com/attribution_link?a=0oFh2Rk3cYo&u=%2Fwatch%3Fv%3DL3RkDChaWgU%26feature%3Dshare
- 11. METH ODS Image acquisition All subjects were scanned on a 3.0 Tesla Philips Intera Achieva Scanner Visual stimuli were generated with a Dell laptop computer running E- Prime software Epson (Model ELP-7000) LCD projector
- 12. METH ODSImaging data analysis Preprocessing and the general linear model The fMRI data were analyzed using Statistical Parametric Mapping software (SPM2, Wellcome Department of Imaging Neuroscience, London, UK). None of the subjects had head movement more than 1.5 mm using the Montreal Neurological Institute (MNI)- 152 template.
- 13. METH ODSImaging data analysis Between subjects correlations predicted that dorsolateral prefrontal cortex (DLPFC) activity would correlate with amygdala EMO task with invariant processing regions (lateral fusiform) during the ID task ID task
- 14. RESUL TSBehavioral results Accuracy A task (emotion, identity) trial (target, non-target) repeated measures ANOVA for accuracy revealed a significant main effect of task (F(1,17)=25.57, p < 0.001), Participants were significantly more accurate for the identity task than the emotion task (p < 0.001). significant task trial interaction (F(1,17)=5.08, p<0.04) mean ± standard error: targets: EMO=87.2% ± 2.2, ID=95.3%±1.5; non-targets: EMO=90.9%±1.3,ID=94.4%±1.6;
- 15. RESUL TSBehavioral results Accuracy Fig. 2. Behaviora Behavioral perfo (mean standard on the emotion a identity 2-back t Participants were significantly mor accurate (A) and faster (B) on the ID task th the EMO task, for all trial types There was no spe Accuracy tradeof in performance b tasks.
- 16. RESUL TSfMRI results EMO>ID task ID>EMO task Conjunction
- 18. DISCU SSION significant increase in activity in DLPFC EMO tasks ID tasks Working Memory increased amygdala activity increased lateral fusiform gyrus activity supporting working memory for the faces and expressions of others.
- 20. N-back is a kind of mental training intended to expand your working memory (WM), and hopefully your intelligence (IQ1). http://www.gwern.net/DNB%20FAQ