Affective brain–computer music interfacing

Daly, Ian; Williams, Duncan; Kirke, Alexis; Weaver, James; Malik, Asad; Hwang, Faustina; Miranda, Eduardo Reck; Nasuto, Slawomir J.

doi:10.1088/1741-2560/13/4/046022

Cited by 49 publications

(54 citation statements)

References 48 publications

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“…The number of electrodes that are used during experimentation in emotion detection situations imposes time constraints on the algorithms. For example, in [44], the authors built a system that detects current user affective states and obtained a classification accuracy of 65%. In [45,46], they tested their method online and offline.…”

Section: Design Innovation (Experimental) Papermentioning

confidence: 99%

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Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

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Section: Design Innovation (Experimental) Papermentioning

confidence: 99%

“…In these studies, brain signals were recorded using an EEG headset while the subject listens to music [44,53,58,100,110,112,115,116,151,154,190,205,216,220,222,235,276,279]. Moreover, the subjects' emotions were recognized as displayed by EEG signals.…”

Section: Domain Description Referencesmentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

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“…The calibration session was designed to identify neural and physiological correlates of emotional responses to music, while subsequent runs were used to identify trajectories for moving between affective states. The details of the complete study are described elsewhere [36], along with a comprehensive discussion of details such as referencing schemes. In this present study we analyse the data obtained during the calibration session to determine whether personalised classification methods for each participant provide better results than generic classifiers for all participants.…”

Section: Methodsmentioning

confidence: 99%

Personalised, Multi-Modal, Affective State Detection for Hybrid Brain-Computer Music Interfacing

Daly

Williams

Malik

et al. 2020

IEEE Trans. Affective Comput.

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Brain-computer music interfaces (BCMIs) may be used to modulate affective states, with applications in music therapy, composition, and entertainment. However, for such systems to work they need to be able to reliably detect their user's current affective state. We present a method for personalised affective state detection for use in BCMI. We compare it to a population-based detection method trained on 17 users and demonstrate that personalised affective state detection is significantly (p < 0.01) more accurate, with average improvements in accuracy of 10.2 % for valence and 9.3 % for arousal. We also compare a hybrid BCMI (a BCMI that combines physiological signals with neurological signals) to a conventional BCMI design (one based upon the use of only EEG features) and demonstrate that the hybrid design results in a significant (p < 0.01) 6.2 % improvement in performance for arousal classification and a significant (p < 0.01) 5.9 % improvement for valence classification.Index Terms-EEG, GSR, Affective state detection, BCMI, Personalised affective state detection. ! • I. Daly is with the Brain-computer interfaces and Neural Engineering laboratory, School

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“…Two facets may drive its intensive interest. On one hand, it enables a wide spectrum of intriguing emotion-oriented applications such as, machine intelligence (Chen et al, 2017), receptionist robots (Pinheiro et al, 2017), content recommendation devices (Lee and Shin, 2013), tutoring systems (Muñoz et al, 2010), and music therapy (Ian et al, 2016). On the other hand, recent explosive innovations in wearable sensing technology considerably bring laboratory-demonstrated emotion-aware research closer to our daily life, necessitating a robust and accurate emotion-aware analytical framework.…”

Section: Introductionmentioning

confidence: 99%

Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis

Lin

Jao

Yang

2017

Front. Comput. Neurosci.

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Constructing a robust emotion-aware analytical framework using non-invasively recorded electroencephalogram (EEG) signals has gained intensive attentions nowadays. However, as deploying a laboratory-oriented proof-of-concept study toward real-world applications, researchers are now facing an ecological challenge that the EEG patterns recorded in real life substantially change across days (i.e., day-to-day variability), arguably making the pre-defined predictive model vulnerable to the given EEG signals of a separate day. The present work addressed how to mitigate the inter-day EEG variability of emotional responses with an attempt to facilitate cross-day emotion classification, which was less concerned in the literature. This study proposed a robust principal component analysis (RPCA)-based signal filtering strategy and validated its neurophysiological validity and machine-learning practicability on a binary emotion classification task (happiness vs. sadness) using a five-day EEG dataset of 12 subjects when participated in a music-listening task. The empirical results showed that the RPCA-decomposed sparse signals (RPCA-S) enabled filtering off the background EEG activity that contributed more to the inter-day variability, and predominately captured the EEG oscillations of emotional responses that behaved relatively consistent along days. Through applying a realistic add-day-in classification validation scheme, the RPCA-S progressively exploited more informative features (from 12.67 ± 5.99 to 20.83 ± 7.18) and improved the cross-day binary emotion-classification accuracy (from 58.31 ± 12.33% to 64.03 ± 8.40%) as trained the EEG signals from one to four recording days and tested against one unseen subsequent day. The original EEG features (prior to RPCA processing) neither achieved the cross-day classification (the accuracy was around chance level) nor replicated the encouraging improvement due to the inter-day EEG variability. This result demonstrated the effectiveness of the proposed method and may shed some light on developing a realistic emotion-classification analytical framework alleviating day-to-day variability.

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Affective brain–computer music interfacing

Cited by 49 publications

References 48 publications

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Personalised, Multi-Modal, Affective State Detection for Hybrid Brain-Computer Music Interfacing

Improving Cross-Day EEG-Based Emotion Classification Using Robust Principal Component Analysis

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