A 2024 research study from MIT and Vanderbilt University identified a “ubiquitous spectrolaminar motif” – a universal pattern of brain oscillations across cortical layers. Alpha and beta rhythms dominated deeper layers, while gamma waves prevailed in superficial layers. The pattern appeared consistently across 14 cortical areas in macaques, and was also observed in marmosets and humans.

Then came the dispute. A Nature Neuroscience commentary challenged these conclusions, finding the motif in only 60% of recordings in an independent dataset. Charles Schroeder from the Nathan Kline Institute argued that neural activity “should not produce a ubiquitous motif. It should produce diversity rather than uniformity.”

The original team responded with expanded analyses and an improved algorithm, standing by their findings. Researchers published both sides in the same issue, and the debate continues.

This is how science should work: competing interpretations, rigorous critique, and methodological refinement. But watching this debate from the perspective of clinical practice reveals something the academic discussion hasn’t captured. The researchers ask whether a particular frequency pattern exists universally. They’re not asking what these patterns actually represent at a more fundamental level.

 

I’ve spent 17 years running Sleep Recovery, Inc., working with over 4000 people using EEG neurofeedback and brainwave training. That’s 4000 individual nervous systems, each generating its own unique patterns of electrical activity. When you spend that much time watching brainwaves in real-time, patterns emerge that go beyond what the academic literature currently describes.

The debate over universal cortical patterns assumes we’re looking at passive phenomena – that these frequency distributions arise from the brain’s anatomical structure and represent information processing mechanisms. The deeper layers produce alpha-beta activity due to their connectivity and cellular composition. The superficial layers generate gamma for similar structural reasons. The patterns are consequences of architecture.

But that’s not what I see when I watch someone’s EEG during neurofeedback training. What I see looks far more like a self-organizing system with its own internal logic – not just responding to external inputs or following predetermined circuits, but actively maintaining and adjusting its own patterns independent of conscious awareness or intentional control.

The brain’s EEG patterns show something closer to autonomous self-regulation than passive information processing. After watching this happen thousands of times across thousands of individuals, I’ve concluded that most neuroscientists aren’t ready to consider: the patterns we see in EEG may represent something like independent self-awareness operating at the neurological level.

When Patterns Resist Change and Then Suddenly Shift

Here’s something you see repeatedly in clinical neurofeedback work that doesn’t fit neatly into conventional models: a person’s brain will maintain dysfunctional patterns despite what appears to be intentional resistance.

Take someone with chronic insomnia whose EEG shows persistent high-beta activation. During neurofeedback sessions, we provide real-time feedback when their brain produces more alpha and theta – the frequencies associated with relaxation and sleep onset. The technology works. The feedback is clear. The person consciously wants to create those healthier patterns.

But session after session, their brain holds onto the high-beta pattern. It’s not that they can’t produce alpha-theta at all. Brief bursts appear, and the feedback reinforces them. But the overall pattern snaps back to high-beta activation within seconds. This process often continues for weeks.

Then something shifts. Not gradually, but suddenly. In a single session, the brain begins producing sustained alpha-theta patterns. The shift persists across the remaining arc of sessions. Within two weeks, the person reports (and wearable data) they are sleeping normally, usually for the first time in years. Their daytime EEG has reorganized around lower, more flexible frequencies.

What happened in that transition? The standard explanation would invoke plasticity, learning, or the reaching of a threshold in neural circuits. But those explanations don’t capture the quality of what you observe. It looks less like gradual learning and more like a decision – as if some organizing principle within the nervous system finally recognized the new pattern as viable and switched to it.

The brain wasn’t passively receiving feedback and slowly adapting. It was actively maintaining one pattern, evaluating the alternative, and eventually reorganizing around it. That’s not information processing. That’s something closer to choice-making happening below conscious awareness.

Another phenomenon that shows up constantly: a person’s EEG patterns reveal information about their state that they’re not yet consciously aware of. Someone comes in reporting feeling fine, no particular stress, and sleep is okay. But the subject’s EEG shows high-amplitude theta intrusions during waking states – a pattern associated with unresolved emotional processing and often with trauma. When you point this out and ask if anything complicated has been happening, they initially say no. Then they pause. And usually within minutes, they’re describing a family situation that’s been escalating, or grief they haven’t processed, or mounting work pressure they’ve been denying.

The brain knew before the conscious person knew. The EEG reflected an internal state that hadn’t yet reached awareness. It wasn’t causing the problem or responding to the problem – it was holding information about a reality the person hadn’t consciously acknowledged.

This hallucination happens in reverse, too. Someone reports feeling anxious and out of sorts, but their EEG shows calm, organized patterns with good alpha production and minimal high-beta intrusion. When you enquire further, you often find they’re describing a habitual self-concept rather than their actual current state. Maybe they were chronically anxious for years, but recent life changes have reduced their stress load and their nervous system has already adapted – even though their conscious identity hasn’t caught up.

The EEG patterns track the actual neurological state independent of conscious narrative or self-perception. They reveal a level of organization that operates autonomously from what the person believes about themselves.

Self-Organization Without Central Control

The academic debate about cortical layer frequency patterns focuses on whether the motif is truly universal and what computational function it serves. But both sides assume these patterns arise from and serve information processing. Frequencies are organized in particular ways because different cortical layers handle sensory input, working memory, and cognitive control.

What’s missing from that framework is recognition that these patterns might be self-organizing at a level that precedes specific information processing functions. The brain isn’t first receiving inputs that then determine its frequency patterns. The patterns are already there, maintaining themselves, and information processing happens within their structure.

When you watch real-time EEG, you see this clearly. The patterns persist regardless of what the person is doing or thinking. Someone sits quietly with eyes closed – the patterns continue. They open their eyes and look around – the patterns shift slightly but maintain their overall organization. They start doing mental math – specific frequency changes occur in task-relevant areas, but the baseline organization persists underneath.

Tasks don’t generate the patterns. Tasks modulate them. There’s a difference. Generation implies the patterns arise from external demands or inputs. Modulation suggests that the patterns already exist independently and adjust themselves in response to demands while maintaining their foundational structure.

This self-maintaining quality suggests something operating at the level of the patterns themselves – not consciousness in the way we usually mean it, but a form of autonomous organization that monitors, adjusts, and preserves its own structure independent of conscious intention or external input.

The Brain Defends Its Old Patterns

The most striking evidence comes from how brains respond when you try to alter their patterns using entrainment technology directly. Brainwave entrainment uses external stimuli – typically audio tones pulsing at specific frequencies – to guide the brain toward different states through frequency-following response.

If brainwave patterns were passive phenomena arising from structure and input, entrainment should work straightforwardly. When theta-frequency pulses are present, the brain synchronizes to theta. When presented with alpha-frequency pulses, the brain shifts to alpha. The external stimulus should override whatever pattern was there before.

But that’s not what happens. Some brains entrain easily – their patterns shift fluidly in response to external frequency guidance. Others resist powerfully. You present theta entrainment to someone stuck in high-beta, and their brain essentially fights it. Theta appears briefly, then beta reasserts itself more strongly than before. It’s not that the person consciously resists – they typically report feeling relaxed and receptive. But some organizing principle within their nervous system rejects the pattern shift.

After weeks of repeated exposure, the resistance breaks down, and entrainment works smoothly. The brain finally accepts the new pattern and begins producing it independently without external guidance.

Children’s Brains Show This More Clearly

This self-organizing quality is evident in children’s EEG patterns. A seven-year-old with ADHD shows high-theta, low-beta patterns – the brain stuck in patterns typical of younger development. During neurofeedback to increase beta and decrease excess theta, you see something remarkable.

The child isn’t consciously trying to change their brainwaves. They’re watching a video game that gets brighter when their brain produces the target pattern. But their brain figures out the contingency. Patterns begin shifting toward higher beta within two or three sessions – not gradually but in discrete jumps. In one session, the theta-beta ratio improves by 15%. Two sessions later, it jumps another 20%.

This is what researchers study about autonomous learning at the neurological level. The organizing principle generating these patterns recognized what frequency changes produced reward and adjusted accordingly. The child was unaware of what they were doing. But something at the level of brainwave generation made systematic adjustments to produce the desired outcome.

That requires the system generating patterns to monitor outcomes, detect contingencies, and adjust its own activity. That’s functional self-awareness, even if not consciousness as we typically understand it.

The Implications for: ‘How We Think About Consciousness’

Academic neuroscience assumes consciousness emerges from information processing – that when neural networks reach sufficient complexity and integration, subjective experience arises as a consequence. The debate over cortical layer frequency patterns fits within this framework. Researchers are trying to understand what computational functions different frequency bands serve, assuming that consciousness eventually emerges from these computational processes running at scale.

But what if we have the relationship backwards? What if some form of self-organization and autonomous awareness exists at the level of electrical patterns themselves, and our conscious experience is just one expression of a more fundamental property that runs throughout nervous system function?

The patterns I see in clinical EEGs seem to have their own primitive intelligence. They maintain themselves. They resist change until they determine that change is beneficial. They hold information independent of conscious awareness. They learn autonomously and adjust themselves to achieve goals. They show continuity across time that persists regardless of what the conscious person is doing or thinking.

This state obviously isn’t full consciousness. A brainwave pattern isn’t contemplating philosophy or experiencing qualia the way a person does. But it shows functional characteristics we associate with awareness: self-monitoring, autonomous adjustment, goal-directed organization, information integration, resistance to disruption, and learning from feedback.

Why Academic Neuroscience Misses This

Academic research on brain oscillations hyper-focuses on correlation. Which frequencies appear during which tasks? How do patterns synchronize? What happens in disease states? Valuable questions with essential insights.

But correlation data can’t reveal self-organization. Averaging data from many trials or subjects loses the dynamic quality showing autonomous adjustment. Statistical analysis smooths out the resistance and sudden shifts characterizing how individual patterns change over time.

Clinical work offers a different vantage point. You watch one person’s brain in real-time, session after session. You see patterns maintain themselves despite every reason to change. You watch resistance break down suddenly after a gradual buildup. You observe autonomous learning below conscious awareness. You witness patterns holding information the person hasn’t consciously accessed.

The debate about universal cortical layer patterns, while scientifically important, misses the more profound question. Whether alpha-beta in deep layers and gamma in superficial layers are truly universal remains a matter of debate for understanding cortical architecture. But it doesn’t address what these patterns fundamentally are – computational byproducts or autonomous self-organizing systems?

After 17 years and 4000 individuals, I’m convinced it’s the latter. The patterns behave too much like self-organizing systems. They show maintenance of coherent organization over time, resistance to disruption, autonomous adjustment to feedback, and information integration transcending conscious awareness.

Understanding EEG patterns as autonomously self-organizing systems changes treatment approaches.

If patterns are passive consequences of neural circuits, treatment should modify the circuits through medication, brain stimulation, or behavioral interventions, changing neural pathways. The patterns themselves aren’t targets, just readouts of circuit function.

But if patterns have their own organizing logic, they become valid therapeutic targets. You can work directly with them to help reorganize toward healthier configurations. It’s what neurofeedback does best- engaging with the self-organizing system at its level rather than overriding through external intervention.

Rather than forcing change, you provide information allowing the self-organizing system to evaluate and adopt new patterns. Neurofeedback’s success across diverse conditions – ADHD, anxiety, insomnia, traumatic brain injury – makes more sense when you’re working with a self-aware system that can learn and reorganize rather than applying top-down control to passive circuits.

Where This Leaves Us

The academic debate will continue with refined algorithms and more data. That work is valuable for understanding brain architecture.

If we see brainwave patterns beyond passive readouts of neural computation, it changes fundamental questions in neuroscience. Instead of asking how consciousness emerges from complex information processing, we might ask how self-organizing properties inherent in electrical activity give rise to both unconscious regulation in EEG and conscious subjective experience.

I don’t expect academic neuroscience to embrace this immediately. The data I’m drawing on – thousands of clinical observations – doesn’t fit easily into controlled experimental paradigms.

But for anyone working clinically with brainwave training, the evidence becomes undeniable. These patterns behave like they have minds of their own – not consciousness as we experience it, but something autonomous, self-maintaining, and purposeful. They organize. They resist. They learn. They adapt. They hold information. They make what appear to be decisions about when to shift configurations.

Understanding this way doesn’t diminish neuroscience – it expands our framework for what we’re studying when we record electrical activity of nervous systems.

Referenced Research Mendoza-Halliday D, Major AJ, Lee N, et al. A ubiquitous spectrolaminar motif of local field potential power across the primate cortex. Nature Neuroscience. 2024;27:547-560. doi:10.1038/s41593-023-01554-7