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One-Class FMRI-Inspired EEG Model for Self-Regulation Training.

Meir-Hasson Y, Keynan JN, Kinreich S, Jackont G, Cohen A, Podlipsky-Klovatch I, Hendler T, Intrator N - PLoS ONE (2016)

Bottom Line: In that work, different models were constructed for different subjects.This EEG based model can overcome substantial limitations of fMRI-NF.It can enable investigation of NF training using multiple sessions and large samples in various locations.

View Article: PubMed Central - PubMed

Affiliation: The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.

ABSTRACT
Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations.

No MeSH data available.


Related in: MedlinePlus

Scheme of the virtual machine used during EEG-NF training.The virtual machine receives the last 3 seconds in the EEG data and returns the predicted BOLD value that corresponds to the last change. a) New EEG segment is attached. b) Preprocessing of the buffer. c-d) Last 12 seconds are multiplied by the cEFP matrix coefficients to calculate the next point on the cEFP signal.
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pone.0154968.g003: Scheme of the virtual machine used during EEG-NF training.The virtual machine receives the last 3 seconds in the EEG data and returns the predicted BOLD value that corresponds to the last change. a) New EEG segment is attached. b) Preprocessing of the buffer. c-d) Last 12 seconds are multiplied by the cEFP matrix coefficients to calculate the next point on the cEFP signal.

Mentions: The virtual machine used to construct the cEFP signal during the real-time NF process is illustrated in Fig 3. The virtual machine received the last 3 seconds in the EEG data and returned the predicted BOLD value that corresponded to the last change. The specific time segment used for the cEFP model (3 seconds) was chosen to match the fMRI time resolution (TR = 3000ms). An equal time resolution between the EEG predictor and the fMRI will enable future validation of the predictive power of the cEFP model. To calculate the next cEFP value, a buffer of the last EEG time series at electrode Pz (12-second-long) was kept in the memory. When a new packet of EEG data arrived (3-second-long), it was attached to the stored buffer. Preprocessing methods applied to the united buffer in real-time were similar to preprocessing methods applied off-line to the EEG data to construct the common model. These include filtering power line noise using a notch filter at 50Hz, converting the EEG time series into a time/frequency representation using the Stockwell transformation (ST), down-sampling the transformation product to 4Hz, reducing the frequency resolution by splitting into 10 frequency bands (defined by the common EFP model), and normalizing by subtracting the mean calculated during a rest session (see [25] for additional information). After applying preprocessing methods on the buffer, the last 12 seconds were multiplied by the cEFP’s matrix coefficients to calculate the next point on the cEFP signal.


One-Class FMRI-Inspired EEG Model for Self-Regulation Training.

Meir-Hasson Y, Keynan JN, Kinreich S, Jackont G, Cohen A, Podlipsky-Klovatch I, Hendler T, Intrator N - PLoS ONE (2016)

Scheme of the virtual machine used during EEG-NF training.The virtual machine receives the last 3 seconds in the EEG data and returns the predicted BOLD value that corresponds to the last change. a) New EEG segment is attached. b) Preprocessing of the buffer. c-d) Last 12 seconds are multiplied by the cEFP matrix coefficients to calculate the next point on the cEFP signal.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4862623&req=5

pone.0154968.g003: Scheme of the virtual machine used during EEG-NF training.The virtual machine receives the last 3 seconds in the EEG data and returns the predicted BOLD value that corresponds to the last change. a) New EEG segment is attached. b) Preprocessing of the buffer. c-d) Last 12 seconds are multiplied by the cEFP matrix coefficients to calculate the next point on the cEFP signal.
Mentions: The virtual machine used to construct the cEFP signal during the real-time NF process is illustrated in Fig 3. The virtual machine received the last 3 seconds in the EEG data and returned the predicted BOLD value that corresponded to the last change. The specific time segment used for the cEFP model (3 seconds) was chosen to match the fMRI time resolution (TR = 3000ms). An equal time resolution between the EEG predictor and the fMRI will enable future validation of the predictive power of the cEFP model. To calculate the next cEFP value, a buffer of the last EEG time series at electrode Pz (12-second-long) was kept in the memory. When a new packet of EEG data arrived (3-second-long), it was attached to the stored buffer. Preprocessing methods applied to the united buffer in real-time were similar to preprocessing methods applied off-line to the EEG data to construct the common model. These include filtering power line noise using a notch filter at 50Hz, converting the EEG time series into a time/frequency representation using the Stockwell transformation (ST), down-sampling the transformation product to 4Hz, reducing the frequency resolution by splitting into 10 frequency bands (defined by the common EFP model), and normalizing by subtracting the mean calculated during a rest session (see [25] for additional information). After applying preprocessing methods on the buffer, the last 12 seconds were multiplied by the cEFP’s matrix coefficients to calculate the next point on the cEFP signal.

Bottom Line: In that work, different models were constructed for different subjects.This EEG based model can overcome substantial limitations of fMRI-NF.It can enable investigation of NF training using multiple sessions and large samples in various locations.

View Article: PubMed Central - PubMed

Affiliation: The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.

ABSTRACT
Recent evidence suggests that learned self-regulation of localized brain activity in deep limbic areas such as the amygdala, may alleviate symptoms of affective disturbances. Thus far self-regulation of amygdala activity could be obtained only via fMRI guided neurofeedback, an expensive and immobile procedure. EEG on the other hand is relatively inexpensive and can be easily implemented in any location. However the clinical utility of EEG neurofeedback for affective disturbances remains limited due to low spatial resolution, which hampers the targeting of deep limbic areas such as the amygdala. We introduce an EEG prediction model of amygdala activity from a single electrode. The gold standard used for training is the fMRI-BOLD signal in the amygdala during simultaneous EEG/fMRI recording. The suggested model is based on a time/frequency representation of the EEG data with varying time-delay. Previous work has shown a strong inhomogeneity among subjects as is reflected by the models created to predict the amygdala BOLD response from EEG data. In that work, different models were constructed for different subjects. In this work, we carefully analyzed the inhomogeneity among subjects and were able to construct a single model for the majority of the subjects. We introduce a method for inhomogeneity assessment. This enables us to demonstrate a choice of subjects for which a single model could be derived. We further demonstrate the ability to modulate brain-activity in a neurofeedback setting using feedback generated by the model. We tested the effect of the neurofeedback training by showing that new subjects can learn to down-regulate the signal amplitude compared to a sham group, which received a feedback obtained by a different participant. This EEG based model can overcome substantial limitations of fMRI-NF. It can enable investigation of NF training using multiple sessions and large samples in various locations.

No MeSH data available.


Related in: MedlinePlus