An EEG Finger-Print of fMRI deep regional activation

Meir-Hasson, Yehudit; Kinreich, Sivan; Podlipsky, Ilana; Hendler, Talma; Intrator, Nathan

doi:10.1016/j.neuroimage.2013.11.004

Cited by 77 publications

(108 citation statements)

References 74 publications

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“…Utilising these data Meir-Hasson et al (2013) applied signal processing and machine learning methods to obtain what they have called an 'EEG Finger Print', whereby a single EEG electrode may be modelled to target brain foci, including deep brain subcortical loci such as the amygdala. They utilised data from a resting alpha study (Ben-Simon et al, 2009) involving the opening and closing of eyes every 30-s for 3-min (the Berger effect).…”

Section: Simultaneous Eeg and Fmri Disclose Alpha/theta Networkmentioning

confidence: 99%

“…This aside, modelling with fMRI (Meir-Hasson et al, 2013) can inform EEG electrode placements, and in turn the EEG can inform a higher temporal resolution than those of fMRI. In fact Zotev et al (2014) have proposed that following their demonstration of dual modality feedback tasks, the tasks may be more effective in combination than either one administered separately.…”

Section: Multi-modal Brain Imaging Feedbackmentioning

confidence: 99%

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EEG-neurofeedback for optimising performance. III: A review of methodological and theoretical considerations

Gruzelier

2014

Neuroscience & Biobehavioral Reviews

229

191

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Section: Simultaneous Eeg and Fmri Disclose Alpha/theta Networkmentioning

confidence: 99%

Section: Multi-modal Brain Imaging Feedbackmentioning

confidence: 99%

EEG-neurofeedback for optimising performance. III: A review of methodological and theoretical considerations

Gruzelier

2014

Neuroscience & Biobehavioral Reviews

229

191

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“…The amyg-EFP model was previously developed by our lab in order to enable the prediction of localized activity in the amygdala using EEG only (Meir-Hasson et al, 2014. This was done by applying machine learning algorithms on EEG data acquired simultaneously with fMRI.…”

Section: The Amyg-efp Modelmentioning

confidence: 99%

“…Keynan et al (2016) validated the reliability of the amyg-EFP in predicting amygdala BOLD activity by conducting further simultaneous EEG-fMRI recordings using a new sample not originally used to develop the model. For further specification, see Meir-Hasson et al (2014 and Keynan et al (2016).…”

Section: The Amyg-efp Modelmentioning

confidence: 99%

“…NF training in the current work targeted downregulation of limbic activity using a novel fMRI-inspired EEG model termed the "amygdala electrical finger print" (amyg-EFP). The amyg-EFP was recently developed in our laboratory to enable accessible limbic targeted NF by applying advanced machine learning algorithms on EEG data acquired simultaneously with fMRI (Meir-Hasson et al, 2014Keynan et al, 2016). We examined whether relative to a standard 2D TM visualization, learning via an interface of complex multi-modal 3D scenario will result in a more effective NF learning as measured by three parameters: (a) learning effect size (b) sustainability, and (c) transferability.…”

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confidence: 99%

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Multi-modal Virtual Scenario Enhances Neurofeedback Learning

et al. 2016

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In the past decade neurofeedback (NF) has become the focus of a growing body of research. With real-time functional magnetic resonance imaging (fMRI) enabling online monitoring of emotion-related areas, such as the amygdala, many have begun testing its therapeutic benefits. However, most existing NF procedures still use monotonic unimodal interfaces, thus possibly limiting user engagement and weakening learning efficiency. The current study tested a novel multi-sensory NF animated scenario (AS) aimed at enhancing user experience and improving learning. We examined whether relative to a simple uni-modal 2D interface, learning via an interface of complex multi-modal 3D scenario will result in improved NF learning. As a neural-probe, we used the recently developed fMRI-inspired EEG model of amygdala activity ("amygdala-EEG finger print"; amygdala-EFP), enabling low-cost and mobile limbic NF training. Amygdala-EFP was reflected in the AS by the unrest level of a hospital waiting room in which virtual characters become impatient, approach the admission desk and complain loudly. Successful downregulation was reflected as an ease in the room unrest level. We tested whether relative to a standard uni-modal 2D graphic thermometer (TM) interface, this AS could facilitate more effective learning and improve the training experience. Thirty participants underwent two separated NF sessions (1 week apart) practicing downregulation of the amygdala-EFP signal. In the first session, half trained via the AS and half via a TM interface. Learning efficiency was tested by three parameters: (a) effect size of the change in amygdala-EFP following training, (b) sustainability of the learned downregulation in the absence of online feedback, and (c) transferability to an unfamiliar context. Comparing amygdala-EFP signal amplitude between the last and the first NF trials revealed that the AS produced a higher effect size. In addition, NF via the AS showed better sustainability, as indicated by a no-feedback trial conducted in session 2 and better transferability to a new unfamiliar interface. Lastly, participants reported that the AS was more engaging and more motivating than the TM. Together, these results demonstrate the promising potential of integrating realistic virtual environments in NF to enhance learning and improve user's experience.

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EEG decoding with spatiotemporal convolutional neural network for visualization and closed‐loop control of sensorimotor activities: A simultaneous EEG‐fMRI study

Iwama,

Tsuchimoto,

Mizuguchi

et al. 2024

Human Brain Mapping

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Closed‐loop neurofeedback training utilizes neural signals such as scalp electroencephalograms (EEG) to manipulate specific neural activities and the associated behavioral performance. A spatiotemporal filter for high‐density whole‐head scalp EEG using a convolutional neural network can overcome the ambiguity of the signaling source because each EEG signal includes information on the remote regions. We simultaneously acquired EEG and functional magnetic resonance images in humans during the brain‐computer interface (BCI) based neurofeedback training and compared the reconstructed and modeled hemodynamic responses of the sensorimotor network. Filters constructed with a convolutional neural network captured activities in the targeted network with spatial precision and specificity superior to those of the EEG signals preprocessed with standard pipelines used in BCI‐based neurofeedback paradigms. The middle layers of the trained model were examined to characterize the neuronal oscillatory features that contributed to the reconstruction. Analysis of the layers for spatial convolution revealed the contribution of distributed cortical circuitries to reconstruction, including the frontoparietal and sensorimotor areas, and those of temporal convolution layers that successfully reconstructed the hemodynamic response function. Employing a spatiotemporal filter and leveraging the electrophysiological signatures of the sensorimotor excitability identified in our middle layer analysis would contribute to the development of a further effective neurofeedback intervention.

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An EEG Finger-Print of fMRI deep regional activation

Cited by 77 publications

References 74 publications

EEG-neurofeedback for optimising performance. III: A review of methodological and theoretical considerations

EEG-neurofeedback for optimising performance. III: A review of methodological and theoretical considerations

Multi-modal Virtual Scenario Enhances Neurofeedback Learning

EEG decoding with spatiotemporal convolutional neural network for visualization and closed‐loop control of sensorimotor activities: A simultaneous EEG‐fMRI study

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