Dr. John Davis Answers Your Neurofeedback Questions

From Clean Signals to Lasting Skills: A Simple Guide to Filters, and Conditioning

Neurofeedback combines precise signal measurement with human learning.

Modern neurofeedback rests on two intertwined foundations. First is signal fidelity: since Hans Berger’s early EEG recordings in the 1920s, engineers and clinicians have refined amplification, electrode placement, and filtering to reliably measure microvolt-level cortical rhythms (Fisch, 1999).

Second is learning theory: mid-20th-century behavior science established how operant reinforcement, classical (respondent) conditioning, planned generalization, and metacognition shape durable behavior change, which neurofeedback leverages to help people discover and reproduce useful brain states in daily life (Skinner, 1953; Stokes & Baer, 1977).

This post is part of a series designed to explain concepts that beginners find challenging.

Filters

What do filters do?

As Fisch (1999) explains:

Filters are used to exclude waveforms of relatively high or low frequency from the EEG so that waveforms in the most important range (1-30 Hz) can be recorded clearly and without distortion. The filters receive the EEG signal after it has passed from the differential amplifier to a second, single-ended, amplifier. After passing through each filter the signal is amplified again by single-ended amplifiers (p. 46).

Neurofeedback uses several common types of filters. A high-pass filter allows frequencies higher than its cutoff point (such as 1 or 2 Hz) to pass through.

A low-pass filter allows frequencies below its cutoff point (such as 30 Hz or higher) to pass through.

A notch filter blocks frequencies around 60 Hz (or 50 Hz outside North America) to prevent electrical interference from power lines from messing up the EEG signal.

Band-pass filters allow practitioners to select a specific frequency range—for instance, 4 to 8 Hz—to define and measure a particular EEG band, such as theta waves.

When the signal requires more amplification, digital filters convert it from an analog (time-based) to a digital (frequency-based) format. Digital filters are essential for transforming the brain’s raw electrical activity into meaningful information for analysis or feedback. Each major filter type has a distinct way of shaping and interpreting EEG signals. Choosing among them involves balancing accuracy, speed, and phase integrity. Graphic © Fouad A. Saad/Shutterstock.com shows the digital reconstruction of an analog waveform.

These filters are categorized into three main types: FIR (finite impulse response), IIR (infinite impulse response), and FFT (fast Fourier transform). Most practitioners use IIR or FFT filters because they better preserve timing information.

FIR Filters

FIR filters produce outputs that fade completely after a brief impulse, depending only on current and past input values. They act like weighted moving averages and are always stable because no feedback loop is used. Their main advantage is a linear phase response, where all frequencies are delayed equally, thereby preserving the waveform shape. This makes FIR filters ideal when timing and phase relationships matter, as in event-related potentials or precise EEG analysis. The trade-off is higher computational demand and longer latency, especially at high filter orders.

IIR Filters

IIR filters, in contrast, feed part of their output back into the input, creating a theoretically infinite response. This feedback allows them to achieve sharp cutoffs with far fewer calculations, making them efficient for real-time use. However, their feedback structure introduces nonlinear phase distortion, shifting different frequencies by slightly different amounts in time. For most neurofeedback applications, where the focus is on band amplitude rather than precise waveform alignment, IIR filters are preferred because they minimize delay and maintain feedback as nearly instantaneous as possible.

Filter Precision is Determined by Their Order

The precision with which IIR and FIR digital filters operate to measure a frequency is determined by their so-called order. The order setting of the filter is simply the number of samples used in the calculation of the output. Samples reflect the sampling rate of the amplifier, which is the number of times the amplifier measures or ‘samples’ the incoming EEG signal. Thus, a higher-order filter uses more samples for calculation, resulting in greater precision.

The Trade-off Between Precision and Speed

However, this precision comes at the expense of speed. A higher-order filter, using more samples (measurements), is also slower to produce an output. For example, a typical 3rd order IIR filter, often used for neurofeedback training, produces an output to the display in 11 ms when the amplifier is sampling the EEG signal at 256 samples per second (sps).

This is fast enough to provide timely feedback to the client so that the changes in the feedback display closely mirror the actual brain-based behavior being measured (sampled). A third-order filter will also accurately represent EEG activity, providing useful information for the feedback display.

When choosing a filter for offline processing of the EEG, where greater precision is desired, a higher-order filter may be used because real-time feedback is not required. Thus, the increased complexity of the calculation with a higher-order filter gives a more precise measurement.

FFT Filters

FFT filters work differently. The fast Fourier transform isn't a filter design but a mathematical algorithm that decomposes time-domain signals into their frequency components. It allows rapid computation of how much power each frequency contributes over short time windows. In neurofeedback, FFT-based processing is widely used to estimate spectral power (e.g., theta, alpha, or beta) in near real-time.

The window length determines how quickly feedback updates: shorter windows provide faster feedback but coarser frequency resolution. FFT filters can vary in complexity, mainly based on the time window chosen for analysis. A Fourier Transform calculation needs at least 1 second of data to work. Most FFT calculations use 2 seconds or more to be more precise.

More complex FFT calculations utilize a technique known as sliding windows. Think of it like taking overlapping snapshots of data—each new window includes some data from the previous one as it "slides" along the data stream. Some people mistakenly think this sliding window approach can produce output as quickly as digital filters (IIR or FIR filters). But this isn't true. No matter how often the window recalculates the data—that is, how quickly it slides along—the math behind FFT still requires at least 1 second of data to create an output.

This creates a problem for neurofeedback training: the data sent to the feedback display is too slow. For effective training, the maximum delay between when something happens in the brain and when the client sees it on the display should be 250-350 milliseconds (Cooper, Heron, & Howard, 2007; Felsinger & Gladstone, 1947; Grice, 1948; Malott & Trojan-Suarez, 2004; Miller, 2006; Miltenberger, 2008). That's about ¼ of a second.

Because of this delay, FFT is better suited for offline data processing, database comparisons, and other tasks—but it's not considered fast enough for neurofeedback training. The one exception is when training slow frequencies below approximately 10 Hz, where the FFT's slower response might be acceptable, although it's still not ideal.

Remember that all filters only reduce (not eliminate) frequencies outside their set boundaries—for example, frequencies outside an 8 to 10 Hz range. While higher-order filters can cut off unwanted frequencies more sharply, they require longer calculation times. Additionally, when using digital filters for multiple frequencies, they must all be of the same type (such as all FFT).

There's one exception to this same-type rule: You can use different calculation types for the same neurofeedback display. For example, you might use 3rd-order IIR filters to provide direct feedback for 4-8 Hz and 15-18 Hz activity in an inhibit/reward display, while also showing the theta/beta power ratio from FFTs for informational purposes.

Filter Comparison

In practice, FIR filters preserve waveform fidelity, IIR filters enable fast and efficient feedback, and FFT filters provide flexible, frequency-based monitoring. Each has its place in neurofeedback: FIR when precision and phase accuracy are critical, IIR when immediacy is crucial, and FFT when spectral tracking is the primary goal. The key principle is consistency—using the same filter type and parameters across sessions ensures that any observed change reflects the brain, not the math.

The accuracy of IIR and FIR digital filters depends on their "order"—basically, how complex their calculations are. Higher numbers mean more complex calculations and more precise measurements. FFT filters also vary in complexity. Remember that filters only reduce (not eliminate) frequencies outside their boundaries—for example, frequencies outside an 8 to 10 Hz range.

While higher-order filters can cut off unwanted frequencies more sharply, they require longer processing times. Additionally, when using digital filters for multiple frequencies, they must all be of the same type (e.g., all FFT).

Phase Distortion and Latency

In neurofeedback, two technical considerations are particularly relevant: phase distortion and latency.

Phase distortion occurs when a filter delays different frequencies by unequal amounts, subtly shifting parts of the EEG waveform in time. This alters the true timing relationship between components of the signal.

Latency refers to the total time delay between when brain activity occurs and when feedback about it is presented to the client.

In neurofeedback, both factors matter because learning depends on the brain recognizing a clear, immediate link between its activity and the feedback it receives. Excessive phase distortion can misrepresent which brain events are being rewarded, and prolonged latency weakens the sense of cause and effect that drives operant learning. Ideally, filters and processing pipelines are designed to minimize both, preserving signal accuracy while keeping total delay short enough (usually <250 ms) for feedback to feel instantaneous and reinforce the intended neural state (Fisch, 1999).

FIR filters are typically linear-phase (phase relationships are preserved), but they may require higher-order filters, which can add computational delay. In contrast, IIR filters achieve sharp transitions with fewer coefficients but introduce frequency-dependent phase shifts (Fisch, 1999).

In feedback learning, contiguity—how immediate the feedback feels relative to neural events—modulates the effectiveness of reinforcement, allowing systems to balance the steepness of attenuation with minimal group delay.

Many platforms compute band activity using short-window FFT power or recursive IIR band envelopes. Either approach is acceptable if applied consistently across sessions, because mixing methods can complicate the interpretation of change over time.

Finally, filters attenuate but do not eliminate artifacts; good technique (e.g., posture, blink strategy, short rest breaks) is still required to maintain high signal quality upstream of the math (Fisch, 1999).

qEEG Cap Placement (precision from landmarks upward)

How should a qEEG cap be put on a client?

Electrode cap graphic © Roman Ziets/Shutterstock.com.

The electrodes in an EEG cap must follow standard 10-20 or 10-10 system placements. 10-20 system graphic adapted from Fisch (1999).

10-10 system graphic adapted from Fisch (1999).

Here's how to do it right: First, choose a cap that fits snugly but isn't too tight. Once it's on, measure where the electrodes sit compared to key bone landmarks—the nasion (bridge of nose), inion (bump at the back of the skull), and preauricular notch (the dip just in front of each ear). Then, adjust the cap so that the electrodes at positions Fp1, Fp2, T3, T4, O1, and O2 are at the correct distance from these landmarks.

The International 10–20 System remains the anchor for reproducible recordings; contemporary nomenclature often labels T3/T4 as T7/T8, but the proportional distances are unchanged. For denser coverage (10–10 or 5% systems), apply the same landmark logic at finer increments to improve spatial sampling for qEEG. Consistent placement stabilizes spectral features, improving longitudinal comparisons and topographic mapping (Acharya, Hani, Cheek, Thirumala, & Tsuchida, 2016).

Reinforcement

Reinforcement through operant conditioning happens when a consequence that follows a behavior makes that behavior more likely to happen again in similar situations. Skinner box graphic © VectorMine/shutterstock.com.

This consequence could be giving a reward (positive reinforcer) or taking away something unpleasant (negative reinforcer). Whether something actually works as a reinforcer depends entirely on whether it changes the behavior—if the behavior increases, it's a reinforcer; if not, it isn't. Due to individual differences, we cannot know in advance whether a consequence will be reinforcing or punishing, as these properties are not inherent to the consequence itself. We can only determine whether a consequence is reinforcing or punishing by measuring its effect on the behavior that preceded it. In neurofeedback, a movie that motivates the best performance might reinforce the client, regardless of the therapist's personal preference.

Positive reinforcement increases the frequency of a desired behavior by making a desired outcome contingent on acting. For example, a movie plays when a client diagnosed with attention deficit hyperactivity disorder (ADHD) increases low-beta and decreases theta activity.

Negative reinforcement increases the frequency of a desired behavior by making the avoidance, termination, or postponement of an unwanted outcome contingent on acting. For example, an athlete's anxiety decreases by shifting from high beta to low beta.

Positive punishment decreases or eliminates an undesirable behavior by associating it with unwanted consequences. For example, a child's increased fidgeting dims the screen and lowers the sound.

Negative punishment decreases or eliminates an undesirable behavior by removing what is desired. For example, oppositional behavior could result in a clinician turning off a popular game.

In neurofeedback, the “behavior” is typically maintaining a target state (e.g., SMR amplitude within a specified range). Visual animations, tones, or points function as reinforcers only if they increase the probability of that state. Three design variables drive results: contingency (tight linkage to the signal), contiguity (minimal delay), and schedule (e.g., small continuous feedback plus intermittent “bonus” events to reduce habituation). These principles map directly from basic operant science to NFB display and scoring choices (Skinner, 1953).

Classical Conditioning

Where does classical conditioning fit into neurofeedback?

Classical conditioning, discovered by Pavlov in 1927 through his famous research with dogs, is an unconscious learning process that establishes connections between events that occur together in time. (Due to faulty English translations, we now say "conditioned" and "unconditioned" instead of Pavlov's original terms "conditional" and "unconditional"—we'll use the current terms to avoid confusion.)

Here's how it works: You start with a neutral stimulus that doesn't cause any response. You repeatedly pair it with something (unconditioned stimulus) that naturally causes a physical response (unconditioned response). Eventually, the neutral stimulus begins to elicit a similar physical response on its own—it becomes a conditioned stimulus that produces a conditioned response.

Pavlov's dogs show this perfectly. Before any training, dogs naturally salivated (an unconditioned response) when they saw food (an unconditioned stimulus). A bell, by itself, meant nothing to them—it was a neutral stimulus that didn't cause salivation. But Pavlov repeatedly rang a bell just before giving the dogs food. Soon, the dogs learned that the bell meant food was on its way. The bell became a conditioned stimulus that elicited salivation (a conditioned response) even before the food appeared. Graphic © VectorMine/iStockphoto.com.

This learning enables us to predict the future based on experience, which is crucial for survival—it gives us time to prepare. But what happens when these predictions stop being accurate?

When the connection between the conditioned stimulus and response breaks down—because the expected outcome no longer follows—the conditioned response may weaken or disappear. This is called extinction. In Pavlov's lab, when the bell repeatedly rang without food following, the dogs eventually stopped salivating. Extinction helps us adapt to changes in our world. You learn to use stronger passwords after identity theft. You can learn to play with dogs again after being bitten as a child.

Interestingly, Pavlov argued that extinction isn't forgetting—it's new learning that overrides old learning. The phenomenon of spontaneous recovery supports this idea: dogs that stopped salivating by the end of a session where no food followed the bell would start salivating again after a break or during the next session.

Two other important processes are generalization and discrimination, which are opposites. Generalization is when we apply what we've learned in one situation to similar situations. Think of Pavlov's dogs: after learning to salivate to a high-pitched bell, they might also salivate to a low-pitched bell. This ability helps us survive—we can apply what we've learned about one danger (like lions) to similar threats (like tigers) without having to experience them directly.

In neurofeedback, there are three types of generalization: stimulus generalization, response generalization, and temporal generalization. Stimulus generalization refers to a trained behavior occurring in situations somewhat different from where it was first learned—this is the most important type for neurofeedback. Response generalization refers to the phenomenon where a slightly different response occurs to the same stimulus. Temporal generalization refers to the behavior continuing after training has ended.

Stimulus generalization is crucial for neurofeedback because the whole point is for beneficial brain states learned in the provider's office to transfer to real-life situations that matter to the client. A client needs to develop those focused attention states from training to work effectively during actual work meetings or school tests.

Here's the key insight: Decades of applied behavior analysis research demonstrate that generalization doesn't occur by accident—it must be deliberately engineered (Stokes & Baer, 1977). Successful neurofeedback practitioners build generalization into their training from the start. They vary the training contexts and task demands. They interleave brief "state probes" to test whether the skill is being transferred. They schedule booster check-ins to help gains last over time.

One powerful tool is creating written If-Then plans that act as portable cues in everyday settings. For example: "If I feel rushed or anxious, then I will take one slow exhale, broaden my visual field, and recall my last training success." These simple action plans help bridge the gap between the training room and the real world, ensuring that the brain states cultivated during neurofeedback become accessible tools for daily life.

Discrimination

Discrimination in classical conditioning is the ability to learn when to be afraid and when to feel safe. It means responding with fear to a real danger signal (CS+, like gunfire) while staying calm for a safe signal (CS−, like fireworks). When this system works properly, the nervous system learns two things: to predict threats from danger cues (CS+) and to feel safe with safety cues (CS−).

In PTSD, research consistently shows that this discrimination system breaks down. People can't properly distinguish between danger and safety signals. Instead, they generalize their fear from actual danger cues to similar but harmless things. That's why fireworks, car backfires, or slamming doors can trigger the same intense response as actual gunfire (Jovanovic, Norrholm, Fani, & Duncan, 2010; Lissek, 2012; Morey, Dunsmoor, Haswell, Brown, McCarthy, & LaBar, 2015).

Scientists measure this problem using the AX+/BX− test. In this test, people learn that the combination "AX" means danger while "BX" means safety. People with PTSD show normal fear to the danger combination (AX), but they can't suppress their fear to the safety combination (BX). This shows they're failing to create or access safety memories (Jovanovic et al., 2010).

In the brain, this pattern involves several regions working incorrectly: the amygdala (fear center) overreacts to conditioned cues, the hippocampus (memory center) struggles with context and overgeneralizes, and the prefrontal cortex (control center) fails to properly engage during safety learning and extinction recall (Dunsmoor & Paz, 2015; Maren, Phan, & Liberzon, 2013; Milad & Quirk, 2012).

From a classical conditioning perspective, neurofeedback works by adjusting conditioned responses and helping the brain retrieve safety memories—not by "reinforcing a behavior" in the operant sense.

EEG-based neurofeedback that promotes alpha or SMR brain states (associated with calm alertness) can reduce overreactions to safety cues and improve the brain's ability to distinguish between real threats and false alarms (Nicholson, Rabellino, Densmore, Frewen, Ros, & Lanius, 2020).

Real-time fMRI neurofeedback takes a more direct approach by training people to reduce amygdala activity while recalling trauma-related images. Studies show this leads to fewer PTSD symptoms and better emotional regulation. This improvement makes sense because it weakens conditioned fear responses to trauma cues and strengthens the ability to recall safety (Zhao, Kirlic, Cosgrove, Craske, Paulus, & Khalsa, 2023; Zotev, Phillips, Young, Drevets, & Bodurka, 2018).

In classical conditioning terms, these neurofeedback protocols aim to reduce fear responses to non-threatening cues and strengthen the brain's processing of safety signals. The goal is to help the nervous system treat fireworks as just fireworks again—not as danger signals requiring a full fear response.

Classical Conditioning in Neurofeedback

In neurofeedback, classical conditioning adds another layer to the learning process. The beneficial brain states shaped through operant conditioning (reward-based training) also become linked through classical conditioning to thoughts, mental images, actions, or situations that happen during training. Later, when those thoughts or situations arise outside of training, they can automatically trigger the same beneficial brain states, thereby increasing the likelihood of success.

For example, a simple state-entry routine—one slow exhale, a softening of gaze, and a cue tone—can be paired with successful brain state changes during training. Eventually, this routine itself will trigger the target brain pattern outside the training room. This classical conditioning layer works in conjunction with operant conditioning to help transfer skills to real-world settings, such as tests, meetings, or performances.

Metacognitive Strategies

What are metacognitive strategies? What is their relationship to neurofeedback?

Metacognition—literally “thinking about thinking”—is the capacity to observe, evaluate, and regulate one’s own mental processes. It encompasses both metacognitive knowledge (understanding how one thinks and learns) and metacognitive control (using that understanding to guide attention, emotion, and behavior). In neurofeedback, metacognition forms the bridge between the neural patterns trained in-session and their application in real-world contexts.

During neurofeedback training, clients learn to recognize subtle internal cues associated with target brain states—such as slower breathing, steadier focus, or a sense of relaxed alertness. Initially, this learning is often implicit: the brain responds to real-time feedback signals without requiring conscious analysis. Over repeated sessions, however, trainees begin to reflect consciously on what “it feels like” when feedback improves. They start asking themselves, What am I doing mentally when the feedback tone becomes steady? What changes when it stops? These reflections mark the emergence of metacognitive awareness.

This awareness enables a critical transition—from automatic conditioning to intentional self-regulation. Once a client can articulate or recognize the qualities of their optimal state (“I widen my attention field,” “I breathe slower,” “My thoughts quiet but stay clear”), they can deliberately re-enter that state outside the training environment. In effect, metacognition turns the feedback system inward: the brain becomes its own trainer. This shift aligns with findings from both cognitive neuroscience and applied biofeedback research showing that explicit self-monitoring amplifies neuroplastic learning.

Metacognition also supports transfer and generalization, two central goals of neurofeedback. In the clinic, clients experience controlled conditions—consistent lighting, minimal distractions, predictable stimuli. Life, by contrast, offers complexity: deadlines, social pressures, and emotional triggers. When clients learn to reflect on their thought processes, they gain a portable toolkit. They can recall, label, and reapply what worked in the lab—voluntarily invoking the neural and psychological patterns associated with successful feedback performance. For instance, a student who trained to stabilize mid-beta rhythms for sustained attention might later recall that same focused, calm state during an exam. A combat veteran who practiced alpha-theta regulation might consciously evoke a slower breathing rhythm and grounded imagery when stress cues arise in traffic.

In this sense, metacognitive skills are the vehicle for neurofeedback transfer. Without it, gains risk remaining context-bound—effective only when the tone or visual reward is present. With it, the individual becomes capable of self-cueing, recognizing when arousal or focus drifts, and applying learned adjustments proactively. This is why advanced neurofeedback protocols often pair signal-based feedback with guided reflection, journaling, or “self-observation” periods between sessions. These strategies cultivate the habit of naming and describing internal shifts, a cornerstone of metacognitive growth.

Moreover, metacognition enriches motivation and self-efficacy. When clients realize that changes in brain activity are linked to their intentional choices and mental habits, they develop a sense of agency—“I can influence my own state.” This belief predicts adherence and long-term benefit. Neuroimaging research supports this, showing that the same prefrontal networks that support metacognitive evaluation (particularly the dorsolateral and anterior cingulate cortices) also participate in attention regulation and error monitoring—two key systems that neurofeedback aims to optimize.

Metacognition also helps refine training itself. Skilled practitioners encourage clients to articulate observations about their mental strategies, enabling iterative protocol adjustments. A client’s comment, such as “When I relax too much, the feedback drops,” provides valuable insight into whether the target frequency represents calm alertness or drowsiness. In this collaborative loop, practitioner and client engage in a metacognitive dialogue, integrating subjective awareness with objective data, thereby transforming neurofeedback into a dynamic learning partnership rather than a passive experience.

In short, metacognitive skills contribute to the longevity of neurofeedback. They transform transient, signal-based learning into a durable, self-directed capability. Clients who cultivate metacognitive awareness do not merely achieve improved EEG coherence or alpha amplitude—they gain an operational language for recognizing and reproducing mental states associated with focus, composure, or creativity. Over time, this capacity generalizes beyond formal training to daily decision-making, interpersonal regulation, and adaptive coping.

Metacognition, then, is not an add-on to neurofeedback—it is the expression of what neurofeedback ultimately teaches: awareness, self-observation, and self-modulation of the brain–mind system in real time.

Key Takeaways

1. Neurofeedback relies on clean signals and fast, faithful processing; the choice of filter (FIR/IIR/FFT), order, and sampling rate shapes both accuracy and delay.

2. For real-time training, low overall latency and minimal phase distortion preserve the brain–feedback link that enables learning; consistency across sessions prevents “math, not mind” effects.

3. Precise 10–20/10–10 placement anchored to cranial landmarks stabilizes topography and supports valid longitudinal/qEEG comparisons.

4. Classical and operant learning both matter: safety learning, discrimination, and planned generalization translate trained states into real-life applications.
   

5. Metacognition transforms neurofeedback gains into lasting self-regulation by teaching clients to recognize, describe, and deliberately recreate the mental states that produce effective brain activity in daily life.

Glossary

amygdala: a limbic structure involved in detecting salience and mediating conditioned fear responses.

analog-to-digital conversion (ADC): a process that converts continuous voltages into discrete samples for digital analysis.

artifact: non-cerebral signal (e.g., eye blinks, muscle, movement) that contaminates the EEG.

band-pass filter: a digital/analog filter that passes only a specified frequency band and attenuates others.

beta waves: EEG activity ≈13–30 Hz associated with alert thinking and task engagement.

classical (respondent) conditioning: cue-outcome learning where a conditioned stimulus predicts an unconditioned outcome and elicits a conditioned response.

coherence: a frequency-specific measure of phase/amplitude coupling between

channels reflecting functional connectivity.

conditioned response (CR): a learned response elicited by a conditioned stimulus after pairing.

conditioned stimulus (CS+ / CS−): a cue paired with an outcome (threat = CS+, safety = CS−) in Pavlovian learning.

contiguity: the immediacy between a neural event and feedback; shorter delays strengthen learning.

contingency: the reliability with which a feedback event depends on a target neural state.

discrimination: the learned ability to respond to danger cues and withhold responses to safety cues.

delta waves: EEG activity ≈0.5–4 Hz common in deep sleep and early development.

electrode montage: a spatial arrangement/reference scheme of recording electrodes.

event-related potential (ERP): a time-locked average of the EEG to discrete events, used for precise latency/phase analysis.

Fast Fourier Transform (FFT): an algorithm that decomposes time-domain data into

frequency components; often used to estimate spectral power.

fear-potentiated startle: a startle reflex amplification to a threat cue; used to assess fear learning and inhibition.

filter order: the number of coefficients/samples used to compute a filter; higher order sharpens transitions but increases computation and delay.

finite impulse response (FIR) filter: a non-recursive filter with finite response; linear phase preserves waveform shape.

frequency resolution: the ability to distinguish nearby frequencies; improves with longer analysis windows.

group delay: the effective time shift introduced by a filter; nonuniform delay across frequencies distorts phase relationships.

hippocampus: a medial temporal structure involved in context processing and generalization gradients.

high-pass filter: a filter that passes frequencies above a cutoff while attenuating slower components.

infinite impulse response (IIR) filter: a recursive filter with feedback; efficient but nonlinear phase can distort timing.

inion: the bony bump on the lower rear of the skull (external occipital protuberance) used as a landmark for EEG electrode placement.

International 10–10 system: a higher-density extension of 10–20 using finer proportional spacing. International 10–20 system: a proportional scalp mapping method using nasion, inion, and preauricular landmarks.

latency: the total delay from neural event to visible/audible feedback; critical to learning.

low-pass filter: a filter that passes frequencies below a cutoff while attenuating faster components.

metacognition: the awareness and regulation of one’s own thinking; noticing, labeling, and adjusting mental states.

negative punishment: in operant conditioning, learning by observing others. For example, a child’s oppositional behavior could result in a clinician turning off a popular game.

negative reinforcement: in operant conditioning, a process that increases the frequency of the desired behavior by making the avoidance, termination, or postponement of an unwanted outcome contingent on acting. For example, an athlete’s anxiety decreases by shifting from high beta to low beta, rewarding this self-regulation.

neutral stimulus (NS): in classical conditioning, a stimulus that does not elicit a response. For example, a bell before pairing with food.

notch filter: a narrowband filter that attenuates a specific frequency (e.g., 50/60 hz mains).

operant conditioning: learning in which consequences alter the probability of a response; used for contingent feedback.

phase distortion: an unequal delay of different frequencies by a filter, altering true temporal relationships.

positive punishment: in operant conditioning, a process that decreases or eliminates an undesirable behavior by associating it with unwanted consequences. For example, a child's increased fidgeting dims the screen and lowers the sound.

positive reinforcement: in operant conditioning, a process that decreases or eliminates an undesirable behavior by associating it with unwanted consequences. For example, a movie plays when a client increases low-beta and decreases theta activity.

prefrontal cortex (PFC): frontal networks supporting cognitive control, error monitoring, and metacognition.

preauricular notch: the palpable depression anterior to the ear; a 10–20 landmark.

qEEG (quantitative EEG): the statistical/spectral analysis of the EEG with topographic mapping and database comparison.

response generalization: the spread of a conditioned response to include other, related reactions to the same cue. After conditioning, the same stimulus may evoke several similar responses—for example, calm breathing and muscle relaxation, both of which are triggered by a relaxation tone in neurofeedback.

sampling rate (sps): the number of measurements per second acquired by the amplifier; sets the Nyquist limit and temporal resolution.

sensorimotor rhythm (SMR): mid-beta activity ≈12–15 Hz over central sites linked to calm, steady attention.

sliding window: overlapping spectral windows that update estimates by stepping through the time series.

spectral leakage: the energy spread across frequency bins due to windowing; reduced by tapering/longer windows.

spectral power: the magnitude of signal energy within a frequency band over time.

spontaneous recovery: the re-emergence of a conditioned response after extinction and a delay; evidence for new learning.

stimulus generalization: the spread of responding to similar cues. A stimulus resembling the original conditioned cue also elicits the learned response—for instance, calm focus from neurofeedback appearing in new settings because environmental cues resemble those from training.

temporal generalization: the persistence of learned responding after training ends across time.

theta waves: EEG activity ≈4–8 Hz associated with drowsiness, memory processes, and some training protocols.

topographic map (topography): the spatial distribution of EEG features across the scalp.

ventromedial prefrontal cortex (vmPFC): a region implicated in safety learning and extinction recall.

window length: the duration of data used per spectral estimate; longer windows improve frequency resolution but increase delay.

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Appreciation

John S. Anderson made significant contributions to this post's discussion of filters.

About the Author

Dr. John Raymond Davis is an adjunct lecturer in the Department of Psychiatry and Behavioural Neurosciences at McMaster University's Faculty of Health Sciences. His scholarly contributions include research on EEG changes in major depression and case studies on neurological conditions. ​

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