Emotion recognition system using brain and peripheral signals: Using correlation dimension to improve the results of EEG

Khalili, Zahra; Moradi, Mohammad Hassan

doi:10.1109/ijcnn.2009.5178854

Cited by 108 publications

(67 citation statements)

References 17 publications

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“…[115,122] Following the theory that emotions emerge as the synchronization of several subsystems, [123] indices of brain areas' synchronization should also be relevant for emotion assessment. This has been demonstrated in [110] by computing inter-electrode mutual information and in [124] by computing the correlation dimension of a set of EEG signals. Several other methods exist to compute synchronization of brain areas [125,126] and these should be tested to confirm the efficiency of this type of feature.…”

Section: Featuresmentioning

confidence: 99%

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Mühl

Allison

Nijholt

et al. 2014

Brain-Computer Interfaces

238

159

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Affective states, moods and emotions, are an integral part of human nature: they shape our thoughts, govern the behavior of the individual, and influence our interpersonal relationships. The last decades have seen a growing interest in the automatic detection of such states from voice, facial expression, and physiological signals, primarily with the goal of enhancing human-computer interaction with an affective component. With the advent of brain-computer interface research, the idea of affective brain-computer interfaces (aBCI), enabling affect detection from brain signals, arose. In this article, we set out to survey the field of neurophysiology-based affect detection. We outline possible applications of aBCI in a general taxonomy of brain-computer interface approaches and introduce the core concepts of affect and their neurophysiological fundamentals. We show that there is a growing body of literature that evidences the capabilities, but also the limitations and challenges of affect detection from neurophysiological activity

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Section: Featuresmentioning

confidence: 99%

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Mühl

Allison

Nijholt

et al. 2014

Brain-Computer Interfaces

238

159

View full text Add to dashboard Cite

show abstract

“…Assuming that in proximity of the above defined medial temporal lobe we could derive some information related to the emotional representation, we extended the set of six electrodes to eight by adding two temporal electrodes (T7 and T8) as also suggested in previous studies [38] [39]. Combining these findings we obtained a pool of eight electrodes: AF3, AF4, F3, F4, F7, F8, T7 and T8.…”

Section: Electrodes Reductionmentioning

confidence: 99%

Individual Classification of Emotions Using EEG

Valenzi¹,

Islam²,

Jurica³

et al. 2014

JBiSE

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Many studies suggest that EEG signals provide enough information for the detection of human emotions with feature based classification methods. However, very few studies have reported a classification method that reliably works for individual participants (classification accuracy well over 90%). Further, a necessary condition for real life applications is a method that allows, irrespective of the immense individual difference among participants, to have minimal variance over the individual classification accuracy. We conducted offline computer aided emotion classification experiments using strict experimental controls. We analyzed EEG data collected from nine participants using validated film clips to induce four different emotional states (amused, disgusted, sad and neutral). The classification rate was evaluated using both unsupervised and supervised learning algorithms (in total seven "state of the art" algorithms were tested). The largest classification accuracy was computed by means of Support Vector Machine. Accuracy rate was on average 97.2%. The experimental protocol effectiveness was further supported by very small variance among individual participants' classification accuracy (within interval: 96.7%, 98.3%). Classification accuracy evaluated on reduced number of electrodes suggested, consistently with psychological constructionist approaches, that we were able to classify emotions considering cortical activity from areas involved in emotion representation. The experimental protocol therefore appeared to be a key factor to improve the classification outcome by means of data quality improvements.

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“…Looking at the corpus reveals that the first cluster comprises two very closely related papers by the same authors, with the titles: 1) A New Discriminant Analysis based on Boundary/NonBoundary Pattern Separation [25] 2) A New Discriminant Analysis for Non-normally Distributed Data based on Datawise Formulation of Scatter Matrices [26] Both papers are focused on feature extraction and clasi- fication. The second cluster comprises abstracts from four papers with titles: 1) Evolutionary Dimensionality Reduction for Crack Localization in Ship Structures using a Hybrid Computational Intelligent Approach [27] 2) Soccer Robot Identification Using Kernel Based Weighted Least Squares [28] 3) Emotion Recognition System Using Brain and Peripheral Signals: Using Correlation Dimension to Improve the Results of EEG [29] 4) CART data analysis to attain interpretability in a Fuzzy Logic Classifier [30] It turns out that the first paper and the last two in this cluster all focus on feature extraction and classificationlike the two papers in the first cluster -while the second by Marins et al is about the system identification of a robot's dynamics.…”

Section: The Text Matrixmentioning

confidence: 99%

Chunks of thought: Finding salient semantic structures in texts

Mei

Vanarase

Minai

2014

2014 International Joint Conference on Neural Networks (IJCNN)

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As the availability of large, digital text corpora increases, so does the need for automatic methods to analyze them and to extract significant information from them. A number of algorithms have been developed for these applications, with topic modeling-based algorithms such as latent Dirichlet allocation (LDA) enjoying much recent popularity. In this paper, we focus on a specific but important problem in text analysis: Identifying coherent lexical combinations that represent "chunks of thought" within the larger discourse. We term these salient semantic chunks (SSCs), and present two complimentary approaches for their extraction. Both these approaches derive from a cognitive rather than purely statistical perspective on the generation of texts. We apply the two algorithms to a corpus of abstracts from IJCNN 2009, and show that both algorithms find meaningful chunks that elucidate the semantic structure of the corpus in complementary ways.

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Emotion recognition system using brain and peripheral signals: Using correlation dimension to improve the results of EEG

Cited by 108 publications

References 17 publications

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

A survey of affective brain computer interfaces: principles, state-of-the-art, and challenges

Individual Classification of Emotions Using EEG

Chunks of thought: Finding salient semantic structures in texts

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