Real-time support vector classification and feedback of multiple emotional brain states

Sitaram, Ranganatha; Lee, Sangkyun; Ruíz, Sergio; Rana, Mohit; Veit, Ralf; Birbaumer, Niels

doi:10.1016/j.neuroimage.2010.08.007

Cited by 180 publications

(160 citation statements)

References 34 publications

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“…One exception is the information regarding the neural correlates associated with recalled happiness (last column of Table 2). Whereas the brain regions for PET studies in Table 1 refer to contrasts between happiness and neutral conditions, such contrasts were reported in only six of the seven fMRI studies (Cerqueira et al, 2008;Cerqueira et al, 2010;Markowitsch, Vandekerckhove, Lanfermann, & Russ, 2003;Pelletier et al, 2003;Zotev et al, 2011;Zotev, Phillips, Yuan, Misaki, & Bodurka, 2014); the final fMRI study (Sitaram, Lee, Ruiz, Rana, Veit, & Birbaumer, 2011) only reported contrasts between three emotional conditions (i.e., happiness, sadness, and disgust). However, since the researchers followed a block-design experimental protocol interleaving emotional and rest conditions, we determined this study to be appropriate for inclusion in this review.…”

Section: Neural Correlates Of Other Emotionsmentioning

confidence: 99%

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The neural correlates of happiness: A review of PET and fMRI studies using autobiographical recall methods

Suardi

Sotgiu

Costa

et al. 2016

Cogn Affect Behav Neurosci

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Although very difficult to define, happiness is becoming a core concept within contemporary psychology and affective neuroscience. In the last two decades, the increased use of neuroimaging techniques has facilitated empirical study of the neural correlates of happiness. This area of research utilizes procedures that induce positive emotion and mood, and autobiographical recall is one of the most widely used and effective approaches. In this article, we review eight positron emission tomography and seven functional magnetic resonance imaging studies that have investigated happiness by using autobiographical recall to induce emotion. Regardless of the neuroimaging technique used, the studies conducted so far have shown that remembering happy events is primarily associated with the activation of many areas, including anterior cingulate cortex, prefrontal cortex, and insula. Importantly, these areas are also found to be connected with other basic emotions, such as sadness and anger. In the conclusion, we integrate these findings, discussing important limitations of the extant literature and suggesting new research directions.Keywords Happiness . Autobiographical recall . PET . fMRI . Mood induction methodsDefining happiness is a difficult task for natural and social scientists, including psychologists, economists, sociologists, geneticists, and neuroscientists. Indeed, happiness is a term often used to refer to and that is interchangeable with many other concepts, such as well-being, flourishing, optimal functioning, life satisfaction, quality of life, and health

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Section: Neural Correlates Of Other Emotionsmentioning

confidence: 99%

“…Three of the seven studies (Markowitsch et al, 2003;Pelletier et al, 2003;Sitaram et al, 2011) contrasted happiness with sadness. However, only two (Cerqueira et al, 2008;Cerqueira et al, 2010) contrasted happiness with anger-related emotions (i.e., irritability), and only one (Sitaram et al, 2011) contrasted happiness and disgust.…”

Section: Emotional Conditionsmentioning

confidence: 99%

The neural correlates of happiness: A review of PET and fMRI studies using autobiographical recall methods

Suardi

Sotgiu

Costa

et al. 2016

Cogn Affect Behav Neurosci

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“…Most psychiatric applications have, so far, aimed to classify individuals into diagnostic groups by their patterns of brain activation or structure [9,10], or to predict treatment response or prognosis [11,12] In the affective domain, a number of studies have attempted to classify the processing of emotion-related information in the human brain, including near-threshold fear [13], pleasantness of thermal stimuli [14], emotional prosody [15], imagery [16], and cross-modal integration of emotional cues from faces and body signals [17]. These studies show that multivariate statistics can predict the valence of sensory stimuli and even internally generated affective states [16] Another open question addressed in the present paper is whether such classification will work across subjects, which will be important for any diagnostic application of normative data. This is an important area of work because valence-driven classification will open up the possibility to perform intra-and inter-individual classification of abnormal affective states using BOLD fMRI signals.…”

Section: Introductionmentioning

confidence: 99%

Pattern classification predicts individuals’ responses to affective stimuli

Yuen¹,

Johnston

Martino

et al. 2012

Translational Neuroscience

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Since the successful demonstration of "brain reading" of fMRI BOLD signals using multivoxel pattern classification (MVPA) techniques, the neuroimaging community has made vigorous attempts to exploit the technique in order to identify the signature patterns of brain activities associated with different cognitive processes or mental states. In the current study, we tested whether the valence and arousal dimensions of the affective information could be used to successfully predict individual's active affective states. Using a whole-brain MVPA approach, together with feature elimination procedures, we are able to discriminate between brain activation patterns associated with the processing of positive or negative valence and cross validate the discriminant function with an independent data set. Arousal information, on the other hand, failed to provide such discriminating power. With an independent sample, we test further whether the MVPA identified brain network could be used for inter-individual classification. Although the inter-subject classification success was only marginal, we found correlations with individual differences in affective processing. We discuss the implications of our findings for future attempts to classify patients based on their responses to affective stimuli.

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“…The LSVM is a modified version of the standard Support Vector Machine (SVM) and is characterized by high accuracy, stable performance, and fast learning speed. Several realtime applications of the SVM method in different fields have been reported in the literature (Sitaram et al, 2011;Gabran et al, 2009). Figure 5 shows the flow chart of the proposed algorithm.…”

Section: Considerations In the Development Of Suction Detection Algormentioning

confidence: 99%

Left Ventricular Assist Devices: Engineering Design Considerations

Simaan¹,

Faragallah²,

Wang³

et al. 2011

New Aspects of Ventricular Assist Devices

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Real-time support vector classification and feedback of multiple emotional brain states

Cited by 180 publications

References 34 publications

The neural correlates of happiness: A review of PET and fMRI studies using autobiographical recall methods

The neural correlates of happiness: A review of PET and fMRI studies using autobiographical recall methods

Pattern classification predicts individuals’ responses to affective stimuli

Left Ventricular Assist Devices: Engineering Design Considerations

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