14 research outputs found
The addition of functional task-oriented mental practice to conventional physical therapy improves motor skills in daily functions after stroke
Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback
Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants' preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others
Effect of the rest interval duration between contractions on muscle fatigue
Background: We aimed to investigate the effect of rest interval, between successive contractions, on muscular fatigue. Methods: Eighteen subjects performed elbow flexion and extension (30 repetitions) on an isokinetic dynamometer with 80 degrees of range of motion. The flexion velocity was 120 degrees/s, while for elbow extension we used 5 different velocities (30, 75, 120, 240, 360 degrees/s), producing 5 different rest intervals (2.89, 1.28, 0.85, 0.57 and 0.54 s). Results: We observed that when the rest interval was 2.89 s there was a reduction in fatigue. On the other hand, when the rest interval was 0.54 s the fatigue was increased. Conclusions: When the resting time was lower (0.54 s) the decline of work in the flexor muscle group was higher compared with different rest interval duration.Universidade do Vale do ParaibaCAPE
Classification of complex emotions using EEG and virtual environment: proof of concept and therapeutic implication
During the last decades, neurofeedback training for emotional self-regulation has
received significant attention from scientific and clinical communities. Most studies have
investigated emotions using functional magnetic resonance imaging (fMRI), including
the real-time application in neurofeedback training. However, the electroencephalogram
(EEG) is a more suitable tool for therapeutic application. Our study aims at establishing a
method to classify discrete complex emotions (e.g., tenderness and anguish) elicited
through a near-immersive scenario that can be later used for EEG-neurofeedback.
EEG-based affective computing studies have mainly focused on emotion classification
based on dimensions, commonly using passive elicitation through single-modality
stimuli. Here, we integrated both passive and active elicitation methods. We
recorded electrophysiological data during emotion-evoking trials, combining emotional
self-induction with a multimodal virtual environment. We extracted correlational and
time-frequency features, including frontal-alpha asymmetry (FAA), using Complex
Morlet Wavelet convolution. Thinking about future real-time applications, we performed
within-subject classification using 1-s windows as samples and we applied trial-specific
cross-validation. We opted for a traditional machine-learning classifier with low
computational complexity and sufficient validation in online settings, the Support Vector
Machine. Results of individual-based cross-validation using the whole feature sets
showed considerable between-subject variability. The individual accuracies ranged from
59.2 to 92.9% using time-frequency/FAA and 62.4 to 92.4% using correlational features.
We found that features of the temporal, occipital, and left-frontal channels were the most
discriminative between the two emotions. Our results show that the suggested pipeline is
suitable for individual-based classification of discrete emotions, paving the way for future
personalized EEG-neurofeedback training
Characterization of interstimulus interaction in the multiple auditory steady-state responses at high sound levels
Data_Sheet_1_Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback.docx
<p>Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants' preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others.</p
Video_1_Emotion Regulation Using Virtual Environments and Real-Time fMRI Neurofeedback.MP4
<p>Neurofeedback (NFB) enables the voluntary regulation of brain activity, with promising applications to enhance and recover emotion and cognitive processes, and their underlying neurobiology. It remains unclear whether NFB can be used to aid and sustain complex emotions, with ecological validity implications. We provide a technical proof of concept of a novel real-time functional magnetic resonance imaging (rtfMRI) NFB procedure. Using rtfMRI-NFB, we enabled participants to voluntarily enhance their own neural activity while they experienced complex emotions. The rtfMRI-NFB software (FRIEND Engine) was adapted to provide a virtual environment as brain computer interface (BCI) and musical excerpts to induce two emotions (tenderness and anguish), aided by participants' preferred personalized strategies to maximize the intensity of these emotions. Eight participants from two experimental sites performed rtfMRI-NFB on two consecutive days in a counterbalanced design. On one day, rtfMRI-NFB was delivered to participants using a region of interest (ROI) method, while on the other day using a support vector machine (SVM) classifier. Our multimodal VR/NFB approach was technically feasible and robust as a method for real-time measurement of the neural correlates of complex emotional states and their voluntary modulation. Guided by the color changes of the virtual environment BCI during rtfMRI-NFB, participants successfully increased in real time, the activity of the septo-hypothalamic area and the amygdala during the ROI based rtfMRI-NFB, and successfully evoked distributed patterns of brain activity classified as tenderness and anguish during SVM-based rtfMRI-NFB. Offline fMRI analyses confirmed that during tenderness rtfMRI-NFB conditions, participants recruited the septo-hypothalamic area and other regions ascribed to social affiliative emotions (medial frontal / temporal pole and precuneus). During anguish rtfMRI-NFB conditions, participants recruited the amygdala and other dorsolateral prefrontal and additional regions associated with negative affect. These findings were robust and were demonstrable at the individual subject level, and were reflected in self-reported emotion intensity during rtfMRI-NFB, being observed with both ROI and SVM methods and across the two sites. Our multimodal VR/rtfMRI-NFB protocol provides an engaging tool for brain-based interventions to enhance emotional states in healthy subjects and may find applications in clinical conditions associated with anxiety, stress and impaired empathy among others.</p
Effect of the rest interval duration between contractions on muscle fatigue
Abstract
Background
We aimed to investigate the effect of rest interval, between successive contractions, on muscular fatigue.
Methods
Eighteen subjects performed elbow flexion and extension (30 repetitions) on an isokinetic dynamometer with 80º of range of motion. The flexion velocity was 120º/s, while for elbow extension we used 5 different velocities (30, 75, 120, 240, 360º/s), producing 5 different rest intervals (2.89, 1.28, 0.85, 0.57 and 0.54 s).
Results
We observed that when the rest interval was 2.89 s there was a reduction in fatigue. On the other hand, when the rest interval was 0.54 s the fatigue was increased.
Conclusions
When the resting time was lower (0.54 s) the decline of work in the flexor muscle group was higher compared with different rest interval duration