Classification of affect using deep learning on brain blood flow data

Bandara, Danushka; Hirshfield, Leanne M.; Velipasalar, Senem

doi:10.1177/0967033519837986

Cited by 12 publications

(4 citation statements)

References 88 publications

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“…EEG and fNIRS data have also been used in studying both within-subject [8] and cross-subject [9] classification scenarios. The authors have identified neural correlates of emotions using fNIRS data across subjects.…”

Section: Crowdsourcing Annotationsmentioning

confidence: 99%

Crowdsourcing Affective Annotations Via fNIRS-BCI

Ruotsalo

Mäkelä

Spapé

2024

IEEE Trans. Affective Comput.

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Affective annotation refers to the process of labeling media content based on the emotions they evoke. Since such experiences are inherently subjective and depend on individual differences, the central challenge is associating digital content with its affective, interindividual experience. Here, we present a first-of-its-kind methodology for affective annotation directly from brain signals by monitoring the affective experience of a crowd of individuals via functional near-infrared spectroscopy (fNIRS). An experiment is reported in which fNIRS was recorded from 31 participants to develop a brain-computer interface (BCI) for affective annotation. Brain signals evoked by images were used to draw predictions about the affective dimensions that characterize the stimuli. By combining annotations, the results show that monitoring crowd responses can draw accurate affective annotations, with performance improving significantly with increases in crowd size. Our methodology demonstrates a proof-of-concept to source affective annotations from a crowd of BCI users without requiring any auxiliary mental or physical interaction.

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Section: Crowdsourcing Annotationsmentioning

confidence: 99%

Crowdsourcing Affective Annotations Via fNIRS-BCI

Ruotsalo

Mäkelä

Spapé

2024

IEEE Trans. Affective Comput.

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“…Some studies have analyzed the cortical activations of subjects to predict emotions. Bandara et al 86 used music videos from the DEAP database 99 to classify the emotional valence and arousal of subjects using a CNN +LSTM architecture and fNIRS data recorded from the prefrontal cortex. Using the subjects' self-assessments as ground-truth, a classification accuracy of 77% was reported.…”

Section: Analysis Of Cortical Activationsmentioning

confidence: 99%

Deep learning in fNIRS: a review

Eastmond

Subedi

et al. 2022

Neurophoton.

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Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional nearinfrared spectroscopy (fNIRS) studies depend heavily on the data processing pipeline and classification model employed. Recently, deep learning (DL) methodologies have demonstrated fast and accurate performances in data processing and classification tasks across many biomedical fields.Aim: We aim to review the emerging DL applications in fNIRS studies.Approach: We first introduce some of the commonly used DL techniques. Then, the review summarizes current DL work in some of the most active areas of this field, including braincomputer interface, neuro-impairment diagnosis, and neuroscience discovery.Results: Of the 63 papers considered in this review, 32 report a comparative study of DL techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of the classification accuracy. In addition, eight studies also utilize DL to reduce the amount of preprocessing typically done with fNIRS data or increase the amount of data via data augmentation. Conclusions:The application of DL techniques to fNIRS studies has shown to mitigate many of the hurdles present in fNIRS studies such as lengthy data preprocessing or small sample sizes while achieving comparable or improved classification accuracy.

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“…Another use of fNIRS is in psychological studies. Bandara et al [261] reported a CNN with Long Short Term Memory (LSTM) to analyze spatiotemporal oxy‐ and deoxy‐ hemodynamics data from the prefrontal cortex for classifying human emotions and achieved 77% accuracy using both oxy‐ and deoxy‐hemoglobin data and 1‐second time steps. These results demonstrate that spatiotemporal features are desired for fNIRS‐based classification tasks, and the DL methods excel in feature exaction in such high dimensional data sets.…”

Section: Applications In Biomedical Opticsmentioning

confidence: 99%

Deep Learning in Biomedical Optics

et al. 2021

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This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC

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Classification of affect using deep learning on brain blood flow data

Cited by 12 publications

References 88 publications

Crowdsourcing Affective Annotations Via fNIRS-BCI

Crowdsourcing Affective Annotations Via fNIRS-BCI

Deep learning in fNIRS: a review

Deep Learning in Biomedical Optics

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