Affective brain-computer interfaces: Psychophysiological markers of emotion in healthy persons and in persons with amyotrophic lateral sclerosis

Nijboer, Femke; Morin, Fabrice O.; Carmien, Stefan; Koene, Randal A.; León, Enrique Chávez; Hoffmann, Ulrich

doi:10.1109/acii.2009.5349479

Cited by 47 publications

(25 citation statements)

References 66 publications

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“…Since in the latest stages of AD communication can be extremely difficult (Miller 1989;Au et al 1988) interaction should be based on channels other than speech (e.g., gestures, emotional expression). A first attempt in this direction has been recently made with ''passive'' or ''affective'' brain-computer interfaces (Nijboer et al 2009), which aim at extracting involuntary brain signals and utilizing them for human-computer interaction. Adapting affective brain-computer interfaces to Alzheimer patients would allow the restoration of some form of communication even in the latest stages of the disease, leading in the future to new paths for cognitive rehabilitation.…”

Section: Discussionmentioning

confidence: 99%

Cognitive reserve and its implications for rehabilitation and Alzheimer’s disease

Liberati¹,

Raffone

Belardinelli

2011

Cogn Process

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According to the Cognitive reserve hypothesis, several factors related to mental engagement, such as level of education, type of occupation, leisure activities and social network, appear to affect the risk of developing clinical dementia. The present article provides an overview of the studies that have investigated the effects of mental engagement and cognitive stimulation specifically on dementia of the Alzheimer's type (AD). Mental training and cognitive stimulation interventions in AD have been shown to be useful in increasing patients' ability in performing activities of daily living (ADL), allowing them to maintain relative independence. Since cognitive engagement and stimulation are known to modify the brain processes to perform tasks, by recruiting alternative and more efficient networks, this review is especially focused on cognitive rehabilitation in AD patients, which has been shown to improve their global functioning and cognition. This perspective stresses the idea that cognitive reserve is not a fixed factor, but can be continuously modified by life experiences, even when the brain is already affected by neuropathology.

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Section: Discussionmentioning

confidence: 99%

Cognitive reserve and its implications for rehabilitation and Alzheimer’s disease

Liberati¹,

Raffone

Belardinelli

2011

Cogn Process

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“…The proposed BCMI system is a form of affective BCI, a BCI intended to interact with the emotions of its user (Nijboer et al 2009). It is also a form of passive BCI, which seeks to modulate a user's affective state based on specific goals (Zander and Kothe 2011), and is different from previous BCMI systems, which are all active BCIs.…”

Section: Introductionmentioning

confidence: 99%

Affective brain–computer music interfacing

et al. 2016

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Abstract. ObjectiveWe aim to develop and evaluate an affective Brain-computer music interface (aBCMI) modulating the affective states of its users. ApproachAn aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithm music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of 8 healthy participants, with each participant returning for multiple sessions. Main ResultsThe final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% (3 class, p< 0.01) and modulate its user's affective states significantly above chance level (p< 0.05). SignificanceOur system represents one of the first demonstrations of an online affective braincomputer music interface that is able to accurately detect and respond to user's affective states. Possible applications include use in music therapy and entertainment.

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“…For example, affective computing focuses on developing applications, which automatically adapts to changes in the user's states, thereby improving interaction that leads to more natural and effective usability (e.g. with games, adjusting to the interest of the user) [1]. Recognizing a user's affective state can be used to optimize training and enhancement of the BCI operations [2].…”

Section: Introductionmentioning

confidence: 99%

Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network

Al-Nafjan¹,

Hosny²,

Al-Wabil³

et al. 2017

ijacsa

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Abstract-Estimationof human emotions from Electroencephalogram (EEG) signals plays a vital role in developing robust Brain-Computer Interface (BCI) systems. In our research, we used Deep Neural Network (DNN) to address EEG-based emotion recognition. This was motivated by the recent advances in accuracy and efficiency from applying deep learning techniques in pattern recognition and classification applications. We adapted DNN to identify human emotions of a given EEG signal (DEAP dataset) from power spectral density (PSD) and frontal asymmetry features. The proposed approach is compared to state-of-the-art emotion detection systems on the same dataset. Results show how EEG based emotion recognition can greatly benefit from using DNNs, especially when a large amount of training data is available.

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Affective brain-computer interfaces: Psychophysiological markers of emotion in healthy persons and in persons with amyotrophic lateral sclerosis

Cited by 47 publications

References 66 publications

Cognitive reserve and its implications for rehabilitation and Alzheimer’s disease

Cognitive reserve and its implications for rehabilitation and Alzheimer’s disease

Affective brain–computer music interfacing

Classification of Human Emotions from Electroencephalogram (EEG) Signal using Deep Neural Network

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