fNIRS Evidence for Recognizably Different Positive Emotions

Hu, Xin; Zhuang, Chu; Wang, Fei; Liu, Yong-Jin; Im, Chang-Hwan; Zhang, Dan

doi:10.3389/fnhum.2019.00120

Cited by 88 publications

(66 citation statements)

References 55 publications

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“…These results are consistent with recent findings showing fNIRS activations in ventrolateral PFC during the viewing of threatening pictures (Tupak et al, 2014). Finally, in a recent study, Hu et al (2019) showed that fNIRS was suitable to isolate the signature of various positive emotions in the PFC. However, some studies did not find differences in Oxy-Hb between baseline and any kind of pictures, whether negative, neutral or positive (Herrmann et al, 2003).…”

Section: Introductionsupporting

confidence: 92%

Human Discrimination and Categorization of Emotions in Voices: A Functional Near-Infrared Spectroscopy (fNIRS) Study

et al. 2020

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Functional Near-Infrared spectroscopy (fNIRS) is a neuroimaging tool that has been recently used in a variety of cognitive paradigms. Yet, it remains unclear whether fNIRS is suitable to study complex cognitive processes such as categorization or discrimination. Previously, functional imaging has suggested a role of both inferior frontal cortices in attentive decoding and cognitive evaluation of emotional cues in human vocalizations. Here, we extended paradigms used in functional magnetic resonance imaging (fMRI) to investigate the suitability of fNIRS to study frontal lateralization of human emotion vocalization processing during explicit and implicit categorization and discrimination using mini-blocks and event-related stimuli. Participants heard speechlike but semantically meaningless pseudowords spoken in various tones and evaluated them based on their emotional or linguistic content. Behaviorally, participants were faster to discriminate than to categorize; and processed the linguistic faster than the emotional content of stimuli. Interactions between condition (emotion/word), task (discrimination/categorization) and emotion content (anger, fear, neutral) influenced accuracy and reaction time. At the brain level, we found a modulation of the Oxy-Hb changes in IFG depending on condition, task, emotion and hemisphere (right or left), highlighting the involvement of the right hemisphere to process fear stimuli, and of both hemispheres to treat anger stimuli. Our results show that fNIRS is suitable to study vocal emotion evaluation, fostering its application to complex cognitive paradigms.

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Section: Introductionsupporting

confidence: 92%

Human Discrimination and Categorization of Emotions in Voices: A Functional Near-Infrared Spectroscopy (fNIRS) Study

et al. 2020

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“…Firstly, the collected fNIRS measurement data were processed with a 0.01-0.2 bandpass filter to remove the instrumental and physiological noises (e.g. heartbeats, respirations and Mayer waves) [31,32,33]. Then, the HbO 2 of each channel was calculated with the filtered data according to the modified Beer-Lambert law [34].…”

Section: Data Processing Preprocessingmentioning

confidence: 99%

Quantified Assessment of Deep Brain Stimulation on Parkinson's Patients with Task fNIRS Measurements and Functional Connectivity Analysis: A Pilot Study

Liang

et al. 2020

Preprint

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Background: Deep brain stimulation (DBS) has proved effective for Parkinsons disease (PD), but the identification of stimulation parameters relies on doctors’ subjective judgment on patient behavior.Methods: Five PD patients performed 10-meter walking tasks under different brain stimulation frequencies. During walking tests, a wearable functional near-infrared spectroscopy (fNIRS) system was used to measure the concentration change of oxygenated hemoglobin (ΔHbO2) in prefrontal cortex, parietal lobe and occipital lobe. Brain functional connectivity and global efficiency were calculated to quantify the brain activities.Results: We discovered that both the global and regional brain efficiency of all patients varied with stimulation parameters, and the DBS pattern enabling the highest brain efficiency was optimal for each patient, in accordance with the clinical assessments and DBS treatment decision made by the doctors.Conclusions: Task fNIRS assessments and brain functional connectivity analysis promise a quantified and objective solution for patient-specific optimization of DBS treatment.Trial registration: The study was approved by the Ethical Committee of Tianjin Huanhu Hospital (2019-35), and has been registered in Chinese Clinical Trial Registry (ChiCTR1900022715).

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“…Finally, heart rate didn't show any effect, but skin conductance response demonstrated a difference although no comparison was done if this adds to EEG or fNIRS. In a more recent study, authors used prefrontal cortex based fNIRS signals recording during emotional video clips to recognize different positive emotions [49]. In this study, we investigated spontaneous facial affective expressions and brain activity simultaneously recorded using both fNIRS and EEG modalities for affective state estimation.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Affective State Assessment Using fNIRS + EEG and Spontaneous Facial Expression

2020

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Human facial expressions are regarded as a vital indicator of one’s emotion and intention, and even reveal the state of health and wellbeing. Emotional states have been associated with information processing within and between subcortical and cortical areas of the brain, including the amygdala and prefrontal cortex. In this study, we evaluated the relationship between spontaneous human facial affective expressions and multi-modal brain activity measured via non-invasive and wearable sensors: functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals. The affective states of twelve male participants detected via fNIRS, EEG, and spontaneous facial expressions were investigated in response to both image-content stimuli and video-content stimuli. We propose a method to jointly evaluate fNIRS and EEG signals for affective state detection (emotional valence as positive or negative). Experimental results reveal a strong correlation between spontaneous facial affective expressions and the perceived emotional valence. Moreover, the affective states were estimated by the fNIRS, EEG, and fNIRS + EEG brain activity measurements. We show that the proposed EEG + fNIRS hybrid method outperforms fNIRS-only and EEG-only approaches. Our findings indicate that the dynamic (video-content based) stimuli triggers a larger affective response than the static (image-content based) stimuli. These findings also suggest joint utilization of facial expression and wearable neuroimaging, fNIRS, and EEG, for improved emotional analysis and affective brain–computer interface applications.

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fNIRS Evidence for Recognizably Different Positive Emotions

Cited by 88 publications

References 55 publications

Human Discrimination and Categorization of Emotions in Voices: A Functional Near-Infrared Spectroscopy (fNIRS) Study

Human Discrimination and Categorization of Emotions in Voices: A Functional Near-Infrared Spectroscopy (fNIRS) Study

Quantified Assessment of Deep Brain Stimulation on Parkinson's Patients with Task fNIRS Measurements and Functional Connectivity Analysis: A Pilot Study

Multimodal Affective State Assessment Using fNIRS + EEG and Spontaneous Facial Expression

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