Neurofeedback simply put, is a tool for strengthening the brain’s attention skills. Using computerized audio/visual feedback, individuals learn to regulate brain functioning by using EEG patterns.
In our style of attention training we are practicing what is called effortless mindfulness1)http://realitysandwich.com/318076/deliberate-mindfulness-and-effortless-mindfulness/. The word “mindfulness” means returning to the object of attention. Alpha-theta training has been used to help athletes and creatives tap into flow states. The alpha/theta
The benefits of Neurofeedback are multifold:
- Better concentration
- Better Attention
- Enhanced Creativity
- Accelerated learning
- Development of self-awareness
- Development of effortless mindfulness
What we teach at Unfetter, is a type of effortless awareness “FLOW” that is different from the cognitive awareness that we normally use for analytical self-related tasks. Deliberate mindfulness requires you to continuously return to the object of meditation. There is a type of “spacious” awareness that we can shift into which becomes the base of effortless mindfulness.
- Integrated exercise of
- Brain processes are only partially constituitive
- Conceptual mistake to superimpose
Attention isn’t a distinct process, but rather a mode where multiple. Mode of processing is many different processes in cognitive unison. Coherence of brain synchrony. It’s a relational phenomena. Dorsolateral PFC .
Michael Anderson and Luiz Pizzoa. Mapping between neural networks and cognitive processes is liekly to be manay-many. Pluripotent (one-many). Degenerate.
Use embodied cognittive science.
- Embodied – Depth perception by the active observer. Self-generated motor activity directly contributes to content of what you see. David McNeill. Embodied langage and thought. Grounded cognition.
- Embedded – Behavior is result of whole
- Extended – Michael Tomasello. Cognitive capacities started with an outside view that you internalize. Metacognitive monitoring dependent on social cognition. Merlin Donald. The human brain is a cultural brain. Adapted to the culture of symbolic culture. Memory isn’t just what’s in head.
- Enactive – Brings forth a lived world
Its constituent cogntive capacityies include internalized forms of social cogntition.
Mindfulness (in any form) is a social practice.
-Context is consitutive
-Context is irreducibly 4E
Looping effects. Dereifying “mindfulness” through analyses of the social factors at work in mindfulness. Track the looping effects of mindfulness practices.
Contemplative Science: The field of science which investigates the neurobiological, psychological, and philosophical facets of introspection.
The field of contemplative science was first conceived by a cognitive neuroscientist named Francisco Varela. “On the one hang, science proceeds because of its pragmatic link to the phenomenal world; indeed, it’s validation is derived from the efficacy of this link.
On the other hand, the tradition of meditative practice proceeds because of its systematic and disciplined link to human experience. The validation of this tradition is derived from it’s ability to transform progressively our lived experience and self-understanding.”
1. The neural origin for compassion is uniquely mammalian and dependent on the phylogenetic changes in the autonomic nervous system from reptiles to mammals.
2. Compassion is “neurophysiologically” incompatible with judgmental, evaluative, and defensive behaviors and feelings that recruit phylogenetically older neural circuits regulating autonomic function.
3. The effectiveness of meditation, listening, chanting, posture and breath on fostering mental states and health is due to a common phylogenetic change in the neural regulation of the ANS.
Unique Mammalian Modifications
Bi-directional interactions among brainstem source nuclei of the myelinated vagus and several cranial nerves that regulate the striated muscles of the face and head result in a “face-heart” connection with “portals” that regulate “state”.
Voice – heart connection (chants)
Listening – heart connection (music)
Breath – heart connection (pranayama)
(Dance and other movements)
Compassion requires turning off defenses
How we feel determines whether we become friends, lovers, or enemies
Our feelings are dependent on our physiological state (autonomic nervous system)
**The sensory processing is bi-directional. 80% of the vagus is sensory.
Defense turns off the mammalian “innovations” of the ANS and the face-heart connection
** People where a polygraph on their face because affect reflects the ANS activity in people. When the face becomes flat because of fear, the neural tone to the heart decreases.
Compassion requires turning off biobehavioral defense systems int he “dyad” to enable both the “compassionate” individual the other to feel safe, to be proximal, and to enable physical contact.
Explains the functional relevance of the mammalian modifications of the ANS and emphasizes the adaptive consequences of detecting risk (ie. safety, danger, or life threat) on physiological experience(including compassion) and health.
- Evolution provides an organizing principle to understand neural regulation of the human ANS. as an enabler of “positive” social behavior.
- Three neural circuits forma phylogentically-ordered response hierarchy that regulate behavioral and physiological adaptation to safe, dangerous, and life-threatening environments. (Jacksonian principle)
- “Neuroception” of danger or safety r life threat trigger these adaptive neural circuits
**Life threat vs. Fight-flight. Relationships are bi-directional
**There is a need to down-regulate mobilization to get people in a safe state. Immobilization with fear. Vasovagal Syncope.
Striated muscles in the face connected to the Corticobulbar pathways. Social engagement. Dyads are important for regulating physiological state.
A Neural Love Code:
Face-to-face interaction. Special visceral efference. Immobilization without fear.
Importance of physical contact while immobilizing without fear. Social engagement and immoiliztion without fear.
Compassion is a manifestation of our biological need to engage and to bond with others. Compassion is a component of our biological quest for “safety” in proximity of another.
The cingulate gyrii lies next to the midline of the brain, just above the corpus callosum. The cingulate contributes to a large network in the brain which integrates the limbic system with the cortex. Among it’s many functions, two pivotal higher order functions are:
- The way the individual responds subconsciously to good or bad outcomes
- The way we express responses to situations that involve suffering, pain, and conflict 1)Austin, 2010
Studies have shown that people who are prone to be worried, “worry-warts”, show increased fMRI signals in both the anterior cingulate and medial prefrontal cortex. 2)M. Paulus, J. Feinstein, A. Simmons, et. al. Anterior cingulate activation in high trait anxious subjects is related to altered error processing during decision making. Biological Psychiatry 2004, 55:1179-1187
Humans and higher primates have a cluster of large spindle-shaped nerve cells int he anterior cingulate and fronto-insular cortex. It’s posited that these cells are in a position to sample emotionally valenced messages related to past experiences and relay them to the frontpolar cortex at Broadmann area 10.
|2.||↑||M. Paulus, J. Feinstein, A. Simmons, et. al. Anterior cingulate activation in high trait anxious subjects is related to altered error processing during decision making. Biological Psychiatry 2004, 55:1179-1187|
One of the goals of many neurofeedback protocols is broadband global synchrony. Nested frequencies often lead to increased phase resets. Particular frequencies can be chose to increase long distance phase synchrony which result in increases in phase resets.
When we couple two different oscillators, their phase relationship can drift apart. A synchronized pulse can shift the phase of one or both of the oscillations, which can result in the oscillators going back in phase or phase-locking for a period of time.
Phase resets occur when the timing of the theta and alpha frequencies simultaneously increase or resets, 2-3 times the normal amplitude. It’s suggested that phase resets are a sign that emotional material is being processed. The subjective accounts are that there has been some type of insight or acceptance.
Dr. Kelly McGonigal speaks about our brain’s reactive tendencies. What does the brain do when we are not asked to do anything in particular? The parietal cortex and hippocampus gives rise to thoughts about the past. The medial prefrontal cortex helps us imagine the future and make informed judgments about the current situation.
What is the “self” of “no-self”?
Farb in 20071)http://intl-scan.oxfordjournals.org/content/early/2007/08/13/scan.nsm030.full wanted to find if there was a part in the brain responsible for the experiential self. The team wanted to find a source for the embodied, present moment self.
The brain works as a system to affect cognition. Dr. Daniel Siegel demonstrates a simple way to teach a model of the brain.
Supervised Vs. Unsupervised Learning
Unsupervised machine learning is separated into two types. Cluster analysis.
1. Throw a bunch of data at the machine and wait for it to come up with an answer for you
2. Throw a bunch of data at the machine and specify a number of X categories
ex. throw a bunch of pictures of males and females, and have the machine separate out into different genders. This is an example of flat clustering.
Flat vs Hierarchical.
Hierarchical means throwing the data at a machine then having the machine figure out how many categories are possible.
Male vs Female face would use flat clustering.
Hierarchical clustering would be used in genomics because we don’t really know how the genes work with each other, and gather insights.
How do we weigh each of these features in importance? We can simplify into 2-3 features that are important.
import numpy as np import matplotlib.pyplot as plt from matplotlib import style style.use(“ggplot”) from sklearn.cluster import KMeans x = [1, 5, 1.5, 8, 1, 9] y = [2, 8, 1.8, 8, 0.6, 11] plt.scatter(x,y) plt.show() X = np.array([1, 2], [5, 8], [1.5, 1.8], [8, 8], [1, 0.6], [9, 11]) kmeans = KMeans(n clusters=3) kmeans.fit(X) centroids = kmeans.cluster_centers_ labels = kmeans.labels_ print(centroids) print(labels) colors = ["g.", "r.", "c.", "y."] for i in range(len(X)): print("coordinate:", X[i], "label:", labels[i]) plt.plot(X[i], X[i], colors[labels[i]], markersize = 10) plt.scatter(centroids[:, 0], centroids[:, 1], marker = "x", s=150, linewidths = 5, zorder = 10) plt.show()
Sometimes we use unsupervised machine learning to visualize a dataset with a lot of features to break it down simply and get the feeling that we are on the right track.
*we group the centroids according to variance. The centroid would be the ideal plot