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<title>Event-related potential</title>

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Event-related potential

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</style><div role="note" class="hatnote navigation-not-searchable">See also: <a href="Evoked_potential" title="Evoked potential">Evoked potential</a></div>

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An event-related potential (ERP) is the measured <a href="Brain" title="Brain">brain</a> response that is the direct result of a specific <a href="Sense" title="Sense">sensory</a>, <a href="Cognition" title="Cognition">cognitive</a>, or <a href="Motor_system" title="Motor system">motor</a> event.<a href="#cite_note-Luck-1">[1]</a> More formally, it is any stereotyped <a href="Electrophysiology" title="Electrophysiology">electrophysiological</a> response to a stimulus. The study of the brain in this way provides a <a href="Invasiveness_of_surgical_procedures" class="mw-redirect" title="Invasiveness of surgical procedures">noninvasive</a> means of evaluating brain functioning.

ERPs are measured by means of <a href="Electroencephalography" title="Electroencephalography">electroencephalography</a> (EEG). The <a href="Magnetoencephalography" title="Magnetoencephalography">magnetoencephalography</a> (MEG) equivalent of ERP is the ERF, or event-related field.<a href="#cite_note-2">[2]</a> <a href="Evoked_potential" title="Evoked potential">Evoked potentials</a> and induced potentials are subtypes of ERPs.

<div class="mw-heading mw-heading2"><h2 id="History">History</h2></div>

With the discovery of the <a href="Electroencephalogram" class="mw-redirect" title="Electroencephalogram">electroencephalogram</a> (EEG) in 1924, <a href="Hans_Berger" title="Hans Berger">Hans Berger</a> revealed that one could measure the electrical activity of the human brain by placing <a href="Electrodes" class="mw-redirect" title="Electrodes">electrodes</a> on the scalp and amplifying the signal. Changes in voltage can then be plotted over a period of time. He observed that the voltages could be influenced by external events that stimulated the senses. The EEG proved to be a useful source in recording brain activity over the ensuing decades. However, it tended to be very difficult to assess the highly specific neural process that are the focus of <a href="Cognitive_neuroscience" title="Cognitive neuroscience">cognitive neuroscience</a> because using pure EEG data made it difficult to isolate individual <a href="Neurocognitive" class="mw-redirect" title="Neurocognitive">neurocognitive</a> processes. Event-related potentials (ERPs) offered a more sophisticated method of extracting more specific sensory, cognitive, and motor events by using simple averaging techniques.

In 1935–1936, Pauline and <a href="Hallowell_Davis" title="Hallowell Davis">Hallowell Davis</a> recorded the first known ERPs on awake humans and their findings were published a few years later, in 1939. Due to <a href="World_War_II" title="World War II">World War II</a> not much research was conducted in the 1940s, but research focusing on sensory issues picked back up again in the 1950s. In 1964, research by <a href="Grey_Walter" class="mw-redirect" title="Grey Walter">Grey Walter</a> and colleagues began the modern era of ERP component discoveries when they reported the first cognitive ERP component, called the <a href="Contingent_negative_variation" title="Contingent negative variation">contingent negative variation</a> (CNV).<a href="#cite_note-3">[3]</a> Sutton, Braren, and Zubin (1965) made another advancement with the discovery of the P3 component.<a href="#cite_note-4">[4]</a> Over the next fifteen years, ERP component research became increasingly popular. The 1980s, with the introduction of inexpensive computers, opened up a new door for cognitive neuroscience research. Currently, ERP is one of the most widely used methods in <a href="Cognitive_neuroscience" title="Cognitive neuroscience">cognitive neuroscience</a> research to study the <a href="Physiological" class="mw-redirect" title="Physiological">physiological</a> correlates of <a href="Sensory_perception" class="mw-redirect" title="Sensory perception">sensory</a>, <a href="Perceptual" class="mw-redirect" title="Perceptual">perceptual</a> and <a href="Cognitive" class="mw-redirect" title="Cognitive">cognitive</a> activity associated with processing information.<a href="#cite_note-5">[5]</a>

<div class="mw-heading mw-heading2"><h2 id="Calculation">Calculation</h2></div>

ERPs can be <a href="Reliability_(statistics)" title="Reliability (statistics)">reliably</a> measured using <a href="Electroencephalograph" class="mw-redirect" title="Electroencephalograph">electroencephalography</a> (EEG), a procedure that measures <a href="Electricity" title="Electricity">electrical</a> activity of the brain over time using <a href="Electrode" title="Electrode">electrodes</a> placed on the <a href="Scalp" title="Scalp">scalp</a>. The EEG reflects thousands of simultaneously <a href="Ongoing_brain_activity" class="mw-redirect" title="Ongoing brain activity">ongoing brain processes</a>. This means that the brain response to a single stimulus or event of interest is not usually visible in the EEG recording of a single trial. To see the brain's response to a stimulus, the experimenter must conduct many trials and average the results together, causing random brain activity to be averaged out and the relevant waveform to remain, called the ERP.<a href="#cite_note-6">[6]</a><a href="#cite_note-7">[7]</a>

The random (<a href="Neural_oscillation#Ongoing_activity" title="Neural oscillation">background</a>) brain activity together with other bio-signals (e.g., <a href="Electrooculography" title="Electrooculography">EOG</a>, <a href="Electromyography" title="Electromyography">EMG</a>, <a href="Electrocardiography" title="Electrocardiography">EKG</a>) and electromagnetic interference (e.g., <a href="Noise_(electronics)" title="Noise (electronics)">line noise</a>, fluorescent lamps) constitute the noise contribution to the recorded ERP. This noise obscures the signal of interest, which is the sequence of underlying ERPs under study.

From an engineering point of view it is possible to define the <a href="Signal-to-noise_ratio" title="Signal-to-noise ratio">signal-to-noise ratio</a> (SNR) of the recorded ERPs. Averaging increases the SNR of the recorded ERPs making them discernible and allowing for their interpretation. This has a simple mathematical explanation provided that some simplifying assumptions are made. These assumptions are:

<ol><li>The signal of interest is made of a sequence of event-locked ERPs with invariable latency and shape</li>

<li>The noise can be approximated by a zero-mean <a href="Gaussian_process" title="Gaussian process">Gaussian random process</a> of variance <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \sigma ^{2}}">

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<annotation encoding="application/x-tex">{\displaystyle \sigma ^{2}}</annotation>

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</math><img src="./53a5c55e536acf250c1d3e0f754be5692b843ef5.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.385ex; height:2.676ex;" alt="{\displaystyle \sigma ^{2}}" loading="lazy"> which is uncorrelated between trials and not time-locked to the event (this assumption can be easily violated, for example in the case of a subject doing little tongue movements while mentally counting the targets in an experiment).</li></ol>

Having defined <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle k}">

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<annotation encoding="application/x-tex">{\displaystyle k}</annotation>

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</math><img src="./c3c9a2c7b599b37105512c5d570edc034056dd40.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.211ex; height:2.176ex;" alt="{\displaystyle k}" loading="lazy">, the trial number, and <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle t}">

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<annotation encoding="application/x-tex">{\displaystyle t}</annotation>

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</math><img src="./65658b7b223af9e1acc877d848888ecdb4466560.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:0.84ex; height:2.009ex;" alt="{\displaystyle t}" loading="lazy">, the time elapsed after the <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle k}">

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<annotation encoding="application/x-tex">{\displaystyle k}</annotation>

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</math><img src="./c3c9a2c7b599b37105512c5d570edc034056dd40.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:1.211ex; height:2.176ex;" alt="{\displaystyle k}" loading="lazy">th event, each recorded trial can be written as <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle x(t,k)=s(t)+n(t,k)}">

</mstyle>

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<annotation encoding="application/x-tex">{\displaystyle x(t,k)=s(t)+n(t,k)}</annotation>

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</math><img src="./866347b46ba23ace5e6ca8085ed000f824efa36c.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:22.191ex; height:2.843ex;" alt="{\displaystyle x(t,k)=s(t)+n(t,k)}" loading="lazy"> where <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle s(t)}">

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<annotation encoding="application/x-tex">{\displaystyle s(t)}</annotation>

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</math><img src="./c484de351ba40ccb9a5ad522c29c1aac5686c0df.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:3.739ex; height:2.843ex;" alt="{\displaystyle s(t)}" loading="lazy"> is the signal and <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle n(t,k)}">

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<annotation encoding="application/x-tex">{\displaystyle n(t,k)}</annotation>

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</math><img src="./d20b1532a2b3ae71a2e73ca1b0a8a38a5d4c6b65.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:6.289ex; height:2.843ex;" alt="{\displaystyle n(t,k)}" loading="lazy"> is the noise (Under the assumptions above, the signal does not depend on the specific trial while the noise does).

The average of <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle N}">

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<annotation encoding="application/x-tex">{\displaystyle N}</annotation>

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<annotation encoding="application/x-tex">{\displaystyle {\bar {x}}(t)={\frac {1}{N}}\sum _{k=1}^{N}x(t,k)=s(t)+{\frac {1}{N}}\sum _{k=1}^{N}n(t,k)}</annotation>

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</math><img src="./61f8c51c5ef28f0cc6bd3ed49ff5c4344248003a.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -3.005ex; width:43.326ex; height:7.343ex;" alt="{\displaystyle {\bar {x}}(t)={\frac {1}{N}}\sum _{k=1}^{N}x(t,k)=s(t)+{\frac {1}{N}}\sum _{k=1}^{N}n(t,k)}" loading="lazy"> .</dd></dl>

The <a href="Expected_value" title="Expected value">expected value</a> of <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle {\bar {x}}(t)}">

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<annotation encoding="application/x-tex">{\displaystyle {\bar {x}}(t)}</annotation>

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</math><img src="./1c1ce141bec4bb83116a4b2188a8cc42365184a3.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:3.979ex; height:2.843ex;" alt="{\displaystyle {\bar {x}}(t)}" loading="lazy"> is (as hoped) the signal itself, <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \operatorname {E} [{\bar {x}}(t)]=s(t)}">

<mover>

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<annotation encoding="application/x-tex">{\displaystyle \operatorname {E} [{\bar {x}}(t)]=s(t)}</annotation>

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</math><img src="./ab98d2ca0f36f88b4c9bf3a14a4bd5651ff7b8b5.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:13.693ex; height:2.843ex;" alt="{\displaystyle \operatorname {E} [{\bar {x}}(t)]=s(t)}" loading="lazy">.

Its <a href="Variance" title="Variance">variance</a> is

<mover>

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<annotation encoding="application/x-tex">{\displaystyle \operatorname {Var} [{\bar {x}}(t)]=\operatorname {E} \left[\left({\bar {x}}(t)-\operatorname {E} [{\bar {x}}(t)]\right)^{2}\right]={\frac {1}{N^{2}}}\operatorname {E} \left[\left(\sum _{k=1}^{N}n(t,k)\right)^{2}\right]={\frac {1}{N^{2}}}\sum _{k=1}^{N}\operatorname {E} \left[n(t,k)^{2}\right]={\frac {\sigma ^{2}}{N}}}</annotation>

</semantics>

</math><img src="./300b505b1bb32ef1d4ae2bed4a27d704c78da9ac.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -3.671ex; width:87.878ex; height:8.509ex;" alt="{\displaystyle \operatorname {Var} [{\bar {x}}(t)]=\operatorname {E} \left[\left({\bar {x}}(t)-\operatorname {E} [{\bar {x}}(t)]\right)^{2}\right]={\frac {1}{N^{2}}}\operatorname {E} \left[\left(\sum _{k=1}^{N}n(t,k)\right)^{2}\right]={\frac {1}{N^{2}}}\sum _{k=1}^{N}\operatorname {E} \left[n(t,k)^{2}\right]={\frac {\sigma ^{2}}{N}}}" loading="lazy">.</dd></dl>

For this reason the noise amplitude of the average of <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle N}">

</mstyle>

</mrow>

<annotation encoding="application/x-tex">{\displaystyle N}</annotation>

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</math><img src="./f5e3890c981ae85503089652feb48b191b57aae3.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.064ex; height:2.176ex;" alt="{\displaystyle N}" loading="lazy"> trials is expected to deviate from the mean (which is <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle s(t)}">

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<annotation encoding="application/x-tex">{\displaystyle s(t)}</annotation>

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</math><img src="./c484de351ba40ccb9a5ad522c29c1aac5686c0df.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:3.739ex; height:2.843ex;" alt="{\displaystyle s(t)}" loading="lazy">) by less or equal than <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \sigma /{\sqrt {N}}}">

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<annotation encoding="application/x-tex">{\displaystyle \sigma /{\sqrt {N}}}</annotation>

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</math><img src="./c6ac45c8fc0d33bb714dee49e2e2b4ab749068a6.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:6.492ex; height:3.176ex;" alt="{\displaystyle \sigma /{\sqrt {N}}}" loading="lazy"> in 68% of the cases. In particular, the deviation wherein 68% of the noise amplitudes lie is <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle 1/{\sqrt {N}}}">

</mrow>

<msqrt>

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<annotation encoding="application/x-tex">{\displaystyle 1/{\sqrt {N}}}</annotation>

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</math><img src="./e08dda9c5205a998e3315b9990800e1a27cfe470.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:6.324ex; height:3.176ex;" alt="{\displaystyle 1/{\sqrt {N}}}" loading="lazy"> times that of a single trial. A larger deviation of <math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle 2\sigma /{\sqrt {N}}}">

</mrow>

<msqrt>

</msqrt>

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<annotation encoding="application/x-tex">{\displaystyle 2\sigma /{\sqrt {N}}}</annotation>

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</math><img src="./b7da44d55e61d32195856943f42362e7936d339a.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:7.654ex; height:3.176ex;" alt="{\displaystyle 2\sigma /{\sqrt {N}}}" loading="lazy"> can already be expected to encompass 95% of all noise amplitudes.

Wide amplitude noise (such as eye blinks or movement <a href="Artifact_(error)" title="Artifact (error)">artifacts</a>) are often several orders of magnitude larger than the underlying ERPs. Therefore, trials containing such artifacts should be removed before averaging. Artifact rejection can be performed manually by visual inspection or using an automated procedure based on predefined fixed thresholds (limiting the maximum EEG amplitude or slope) or on time-varying thresholds derived from the statistics of the set of trials.

<div class="mw-heading mw-heading2"><h2 id="Nomenclature">Nomenclature</h2></div>

ERP waveforms consist of a series of positive and negative voltage deflections, which are related to a set of underlying components.<a href="#cite_note-8">[8]</a> Though some ERP components are referred to with acronyms (e.g., <a href="Contingent_negative_variation" title="Contingent negative variation">contingent negative variation</a> – CNV, <a href="Error-related_negativity" title="Error-related negativity">error-related negativity</a> – ERN), most components are referred to by a letter (N/P) indicating polarity (negative/positive), followed by a number indicating either the latency in milliseconds or the component's <a href="Ordinal_number" title="Ordinal number">ordinal</a> position in the waveform. For instance, a negative-going peak that is the first substantial peak in the waveform and often occurs about 100 milliseconds after a stimulus is presented is often called the <a href="N100_(neuroscience)" class="mw-redirect" title="N100 (neuroscience)">N100</a> (indicating its latency is 100 ms after the stimulus and that it is negative) or N1 (indicating that it is the first peak and is negative); it is often followed by a positive peak, usually called the <a href="P200" title="P200">P200</a> or P2. The stated latencies for ERP components are often quite variable, particularly so for the later components that are related to the cognitive processing of the stimulus. For example, the <a href="P300_(neuroscience)" title="P300 (neuroscience)">P300</a> component may exhibit a peak anywhere between 250 ms – 700 ms.

<div class="mw-heading mw-heading2"><h2 id="Advantages_and_disadvantages">Advantages and disadvantages</h2></div>

<div class="mw-heading mw-heading3"><h3 id="Relative_to_behavioral_measures">Relative to behavioral measures</h3></div>

Compared with behavioral procedures, ERPs provide a continuous measure of processing between a stimulus and a response, making it possible to determine which stage(s) are being affected by a specific experimental manipulation. Another advantage over behavioral measures is that they can provide a measure of processing of stimuli even when there is no behavioral change. However, because of the significantly small size of an ERP, it usually takes a large number of trials to accurately measure it correctly.<a href="#cite_note-9">[9]</a>

<div class="mw-heading mw-heading3"><h3 id="Relative_to_other_neurophysiological_measures">Relative to other neurophysiological measures</h3></div>

<div class="mw-heading mw-heading4"><h4 id="Invasiveness">Invasiveness</h4></div>

Unlike microelectrodes, which require an electrode to be inserted into the brain, and <a href="Positron_emission_tomography" title="Positron emission tomography">PET</a> scans that expose humans to radiation, ERPs use EEG, a non-invasive procedure.

<div class="mw-heading mw-heading4"><h4 id="Spatial_and_temporal_resolution">Spatial and temporal resolution</h4></div>

ERPs provide excellent <a href="Temporal_resolution" title="Temporal resolution">temporal resolution</a>—as the speed of ERP recording is only constrained by the sampling rate that the recording equipment can feasibly support, whereas <a href="Hemodynamic" class="mw-redirect" title="Hemodynamic">hemodynamic</a> measures (such as <a href="FMRI" class="mw-redirect" title="FMRI">fMRI</a>, <a href="Positron_emission_tomography" title="Positron emission tomography">PET</a>, and <a href="Functional_near_infrared_spectroscopy" class="mw-redirect" title="Functional near infrared spectroscopy">fNIRS</a>) are inherently limited by the slow speed of the <a href="Blood-oxygen-level_dependent" class="mw-redirect" title="Blood-oxygen-level dependent">BOLD</a> response. The <a href="Spatial_resolution" title="Spatial resolution">spatial resolution</a> of an ERP, however, is much poorer than that of hemodynamic methods—in fact, the location of ERP sources is an <a href="Inverse_problem" title="Inverse problem">inverse problem</a> that cannot be exactly solved, only estimated. Thus, ERPs are well suited to research questions about the speed of neural activity, and are less well suited to research questions about the location of such activity.<a href="#cite_note-Luck-1">[1]</a>

ERP research is much cheaper to do than other imaging techniques such as <a href="FMRI" class="mw-redirect" title="FMRI">fMRI</a>, <a href="Positron_emission_tomography" title="Positron emission tomography">PET</a>, and <a href="Magnetoencephalography" title="Magnetoencephalography">MEG</a>. This is because purchasing and maintaining an EEG system is less expensive than the other systems.

<div class="mw-heading mw-heading2"><h2 id="Clinical">Clinical</h2></div>

<a href="Physician" title="Physician">Physicians</a> and <a href="Neurology" title="Neurology">neurologists</a> will sometimes use a flashing <a href="Visual_perception" title="Visual perception">visual</a> checkerboard stimulus to test for any damage or trauma in the visual system. In a healthy person, this stimulus will elicit a strong response over the primary <a href="Visual_cortex" title="Visual cortex">visual cortex</a> located in the <a href="Occipital_lobe" title="Occipital lobe">occipital lobe</a>, in the back of the brain.

ERP component abnormalities in clinical research have been shown in neurological conditions such as:

<ul><li><a href="Dementia" title="Dementia">Dementia</a><a href="#cite_note-11">[11]</a><a href="#cite_note-12">[12]</a></li></ul>

<ul><li><a href="Parkinson's_disease" title="Parkinson's disease">Parkinson's disease</a><a href="#cite_note-13">[13]</a></li>

<li><a href="Multiple_sclerosis" title="Multiple sclerosis">Multiple sclerosis</a><a href="#cite_note-14">[14]</a></li>

<li>Head injuries<a href="#cite_note-15">[15]</a></li>

<li>Stroke<a href="#cite_note-16">[16]</a></li>

<li><a href="Obsessive-compulsive_disorder" class="mw-redirect" title="Obsessive-compulsive disorder">Obsessive-compulsive disorder</a><a href="#cite_note-17">[17]</a></li>

<li><a href="Schizophrenia" title="Schizophrenia">Schizophrenia</a><a href="#cite_note-18">[18]</a><a href="#cite_note-19">[19]</a></li>

<li><a href="Depression_(mood)" title="Depression (mood)">Depression</a><a href="#cite_note-:0-20">[20]</a></li>

<li><a href="Autism_spectrum_disorder" class="mw-redirect" title="Autism spectrum disorder">Autism spectrum disorder</a><a href="#cite_note-21">[21]</a></li>

<li><a href="Epilepsy" title="Epilepsy">Epilepsy</a> – to monitor the efficiency of cognitive processes<a href="#cite_note-22">[22]</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="Research">Research</h2></div>

ERPs are used extensively in <a href="Neuroscience" title="Neuroscience">neuroscience</a>, <a href="Cognitive_psychology" title="Cognitive psychology">cognitive psychology</a>, <a href="Cognitive_science" title="Cognitive science">cognitive science</a>, and <a href="Psychophysiology" title="Psychophysiology">psycho-physiological</a> research. <a href="Experimental_psychology" title="Experimental psychology">Experimental psychologists</a> and <a href="Neuroscientist" title="Neuroscientist">neuroscientists</a> have discovered many different stimuli that elicit reliable ERPs from participants. The timing of these responses is thought to provide a measure of the timing of the brain's communication or timing of information processing. For example, in the checkerboard paradigm described above, healthy participants' first response of the visual cortex is around 50–70 ms. This would seem to indicate that this is the amount of time it takes for the <a href="Transduction_(physiology)" title="Transduction (physiology)">transduced</a> visual stimulus to reach the <a href="Telencephalon" class="mw-redirect" title="Telencephalon">cortex</a> after <a href="Light" title="Light">light</a> first enters the <a href="Eye" title="Eye">eye</a>. Alternatively, the <a href="P300_(neuroscience)" title="P300 (neuroscience)">P300</a> response occurs at around 300ms in the <a href="Oddball_paradigm" title="Oddball paradigm">oddball paradigm</a>, for example, regardless of the type of stimulus presented: <a href="Visual_system" title="Visual system">visual</a>, <a href="Tactition" class="mw-redirect" title="Tactition">tactile</a>, <a href="Sound" title="Sound">auditory</a>, <a href="Olfaction" class="mw-redirect" title="Olfaction">olfactory</a>, <a href="Gustatory" class="mw-redirect" title="Gustatory">gustatory</a>, etc. Because of this general invariance with regard to stimulus type, the P300 component is understood to reflect a higher cognitive response to unexpected and/or cognitively <a href="Salience_(neuroscience)" title="Salience (neuroscience)">salient</a> stimuli. The P300 response has also been studied in the context of information and memory detection.<a href="#cite_note-23">[23]</a> In addition, there are studies on abnormalities of P300 in depression. Depressed patients tend to have a reduced P200 and P300 amplitude and a prolonged P300 latency.<a href="#cite_note-:0-20">[20]</a>

Due to the consistency of the P300 response to novel stimuli, a <a href="Brain%E2%80%93computer_interface" title="Brain–computer interface">brain–computer interface</a> can be constructed which relies on it. By arranging many signals in a grid, randomly flashing the rows of the grid as in the previous paradigm, and observing the P300 responses of a subject staring at the grid, the subject may communicate which stimulus he is looking at, and thus slowly "type" words.<a href="#cite_note-24">[24]</a>

Another area of research in the field of ERP lies in the <a href="Efference_copy" title="Efference copy">efference copy</a>. This predictive mechanism plays a central role in for example human verbalization.<a href="#cite_note-25">[25]</a><a href="#cite_note-26">[26]</a> Efference copies, however, do not only occur with spoken words, but also with inner language - i.e. the quiet production of words - which has also been proven by event-related potentials.<a href="#cite_note-27">[27]</a>

Other ERPs used frequently in research, especially <a href="Neurolinguistics" title="Neurolinguistics">neurolinguistics research</a>, include the <a href="ELAN_(neurolinguistics)" class="mw-redirect" title="ELAN (neurolinguistics)">ELAN</a>, the <a href="N400_(neuroscience)" title="N400 (neuroscience)">N400</a>, and the <a href="P600_(neuroscience)" title="P600 (neuroscience)">P600/SPS</a>. The analysis of ERP data is also increasingly supported by machine learning algorithms.<a href="#cite_note-28">[28]</a><a href="#cite_note-29">[29]</a>

<div class="mw-heading mw-heading3"><h3 id="Number_of_trials">Number of trials</h3></div>

A common issue in ERP studies is whether the observed data have a sufficient number of trials to support statistical analysis.<a href="#cite_note-:1-30">[30]</a> The background noise in any ERP for any individual can vary. Therefore simply characterizing the number of ERP trials needed for a robust component response is inadequate. ERP researchers can use metrics like the standardized measurement error (SME) to justify the examination of between-condition or between-group differences<a href="#cite_note-31">[31]</a> or estimates of internal consistency to justify the examination of individual differences.<a href="#cite_note-32">[32]</a><a href="#cite_note-33">[33]</a><a href="#cite_note-:1-30">[30]</a>

<ul><li><a href="Bereitschaftspotential" title="Bereitschaftspotential">Bereitschaftspotential</a></li>

<li><a href="Contingent_negative_variation" title="Contingent negative variation">Contingent negative variation</a></li>

<li><a href="Difference_due_to_memory" title="Difference due to memory">Difference due to memory</a></li>

<li><a href="Early_left_anterior_negativity" title="Early left anterior negativity">Early left anterior negativity</a></li>

<li><a href="Erich_Schr%C3%B6ger" title="Erich Schröger">Erich Schröger</a></li>

<li><a href="Error-related_negativity" title="Error-related negativity">Error-related negativity</a></li>

<li><a href="Evoked_potential" title="Evoked potential">Evoked potential</a></li>

<li><a href="Induced_activity" class="mw-redirect" title="Induced activity">Induced activity</a></li>

<li><a href="Lateralized_readiness_potential" title="Lateralized readiness potential">Lateralized readiness potential</a></li>

<li><a href="Mismatch_negativity" title="Mismatch negativity">Mismatch negativity</a></li>

<li>Negativity: <a href="N100_(neuroscience)" class="mw-redirect" title="N100 (neuroscience)">N100</a> • <a href="Visual_N1" title="Visual N1">Visual N1</a> • <a href="N170" title="N170">N170</a> • <a href="N200_(neuroscience)" title="N200 (neuroscience)">N200</a> • <a href="N2pc" title="N2pc">N2pc</a> • <a href="N400_(neuroscience)" title="N400 (neuroscience)">N400</a></li>

<li>Positivity: <a href="P200" title="P200">P200</a> • <a href="P300_(neuroscience)" title="P300 (neuroscience)">P300</a> • <a href="P3a" title="P3a">P3a</a> • <a href="P3b" title="P3b">P3b</a> • <a href="Late_positive_component" title="Late positive component">Late positive component</a> • <a href="P600_(neuroscience)" title="P600 (neuroscience)">P600</a></li>

<li><a href="Somatosensory_evoked_potential" title="Somatosensory evoked potential">Somatosensory evoked potential</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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</ol></div></div>

<div class="mw-heading mw-heading2"><h2 id="Further_reading">Further reading</h2></div>

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<ul><li><cite id="CITEREFLuck2014" class="citation book cs1">Luck SJ (2014). <a rel="nofollow" class="external text" href="https://web.archive.org/web/20180317063618/https://mitpress.mit.edu/books/introduction-event-related-potential-technique-0">An Introduction to the Event-Related Potential Technique</a> (Second ed.). Cambridge, Massachusetts: The MIT Press. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-262-52585-5</bdi>. Archived from <a rel="nofollow" class="external text" href="https://mitpress.mit.edu/books/introduction-event-related-potential-technique-0">the original</a> on 2018-03-17. Retrieved 2017-03-28.</cite></li>

<li><cite id="CITEREFLuckKappenman2005" class="citation book cs1">Luck SJ, Kappenman ES, eds. (2005). <a rel="nofollow" class="external text" href="http://www.oup.com/us/catalog/general/subject/Psychology/CognitivePsychology/?view=usa&ci=9780195374148">The Oxford Handbook of Event-Related Potential Components</a>. Cambridge, Mass.: MIT Press. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-262-08333-1</bdi>.</cite></li>

<li><cite id="CITEREFFabianiGrattonFedermeier2007" class="citation book cs1">Fabiani M, Gratton G, Federmeier KD (2007). <a rel="nofollow" class="external text" href="https://books.google.com/books?id=E7hRKwVBXb4C&pg=PA85">"Event-Related Brain Potentials: Methods, Theory, and Applications"</a>. In Cacioppo JT, Tassinary LG, Berntson GG (eds.). Handbook of Psychophysiology (3rd ed.). Cambridge: Cambridge University. pp. 85–119. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-521-84471-0</bdi>.</cite></li>

<li><cite id="CITEREFPolichCorey-Bloom2005" class="citation journal cs1">Polich J, Corey-Bloom J (December 2005). "Alzheimer's disease and P300: review and evaluation of task and modality". Current Alzheimer Research. 2 (5): 515–25. <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.2174%2F156720505774932214">10.2174/156720505774932214</a>. <a href="PMID_(identifier)" class="mw-redirect" title="PMID (identifier)">PMID</a> <a rel="nofollow" class="external text" href="https://pubmed.ncbi.nlm.nih.gov/16375655">16375655</a>.</cite></li>

<li><cite id="CITEREFZaniProverbio2003" class="citation book cs1">Zani A, Proverbio AM (2003). Cognitive Electrophysiology of Mind and Brain. Amsterdam: Academic Press. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-12-775421-5</bdi>.</cite></li>

<li><cite id="CITEREFKropotov2009" class="citation book cs1">Kropotov J (2009). Quantitative EEG, Event-Related Potentials and Neurotherapy (1st ed.). Amsterdam: Elsevier/Academic. <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>978-0-12-374512-5</bdi>.</cite></li></ul>

</div>

<div class="mw-heading mw-heading2"><h2 id="External_links">External links</h2></div>

<ul><li><a rel="nofollow" class="external autonumber" href="http://erpsummerschool.bangor.ac.uk/">[1]</a> – ERP Summer School 2017 was held in The School of Psychology, Bangor University from 25–30 June 2017</li>

<li><a rel="nofollow" class="external text" href="http://sccn.ucsd.edu/eeglab/">EEGLAB Toolbox</a> – A freely available, open-source, Matlab toolbox for processing and analyzing EEG data</li>

<li><a rel="nofollow" class="external text" href="http://erpinfo.org/erplab">ERPLAB Toolbox</a> – A freely available, open-source, Matlab toolbox for processing and analyzing ERP data</li>

<li><a rel="nofollow" class="external text" href="http://erpinfo.org/the-erp-bootcamp">The ERP Boot Camp</a> <a rel="nofollow" class="external text" href="https://web.archive.org/web/20161128191101/http://erpinfo.org/the-erp-bootcamp">Archived</a> 2016-11-28 at the <a href="Wayback_Machine" title="Wayback Machine">Wayback Machine</a> – A series of training workshops for ERP researchers led by Steve Luck and Emily Kappenman</li>

<li><a rel="nofollow" class="external text" href="https://erpinfo.org/blog/">Virtual ERP Boot Camp</a> – A blog with information, announcements, and tips about ERP methodology</li></ul>

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</style><div id="Electroencephalography_(EEG)118" style="font-size:114%;margin:0 4em"><a href="Electroencephalography" title="Electroencephalography">Electroencephalography (EEG)</a></div></th></tr><tr><th scope="row" class="navbox-group" style="width:1%">Related tests</th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Amplitude_integrated_electroencephalography" title="Amplitude integrated electroencephalography">Amplitude integrated electroencephalography (aEEG)</a></li>

<li><a href="Electrocorticography" title="Electrocorticography">Electrocorticography (ECoG)</a></li>

<li><a href="Magnetoencephalography" title="Magnetoencephalography">Magnetoencephalography (MEG)</a></li>

<li><a href="Somatosensory_evoked_potential" title="Somatosensory evoked potential">Somatosensory evoked potential</a></li>

<li><a href="Brainstem_auditory_evoked_potential" title="Brainstem auditory evoked potential">Brainstem auditory evoked potential</a></li></ul>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Evoked_potential" title="Evoked potential">Evoked potentials</a></th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<dl><dt>Negativity</dt>

<dd><a href="Bereitschaftspotential" title="Bereitschaftspotential">Bereitschaftspotential</a></dd>

<dd><a href="Visual_N1" title="Visual N1">Visual N1</a></dd>

<dd><a href="Contingent_negative_variation" title="Contingent negative variation">Contingent negative variation (CNV)</a></dd>

<dd><a href="Mismatch_negativity" title="Mismatch negativity">Mismatch negativity</a></dd></dl>

<dl><dt>Positivity</dt>

<dd><a href="P600_(neuroscience)" title="P600 (neuroscience)">P600</a> (late positivity)</dd>

<dd><a href="Late_positive_component" title="Late positive component">Late positive component</a></dd></dl>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%"><a href="Neural_oscillation" title="Neural oscillation">Neural oscillations</a></th><td class="navbox-list-with-group navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Alpha_wave" title="Alpha wave">Alpha wave</a></li>

<li><a href="Gamma_wave" title="Gamma wave">Gamma wave</a></li>

<li><a href="Delta_wave" title="Delta wave">Delta wave</a></li>

<li><a href="Theta_rhythm" class="mw-redirect" title="Theta rhythm">Theta rhythm</a></li>

<li><a href="K-complex" title="K-complex">K-complex</a></li>

<li><a href="Sleep_spindle" title="Sleep spindle">Sleep spindle</a></li>

<li><a href="Sensorimotor_rhythm" title="Sensorimotor rhythm">Sensorimotor rhythm</a></li>

</div></td></tr><tr><th scope="row" class="navbox-group" style="width:1%">Topics</th><td class="navbox-list-with-group navbox-list navbox-even hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="10-20_system_(EEG)" class="mw-redirect" title="10-20 system (EEG)">10-20 system</a></li>

<li><a href="Difference_due_to_memory" title="Difference due to memory">Difference due to memory (Dm)</a></li>

<li><a href="Oddball_paradigm" title="Oddball paradigm">Oddball paradigm</a></li>

<li><a href="EEGLAB" title="EEGLAB">EEGLAB</a></li>

<li><a href="Neurophysiological_Biomarker_Toolbox" title="Neurophysiological Biomarker Toolbox">Neurophysiological Biomarker Toolbox (NBT)</a></li></ul>

</div></td></tr></tbody></table></div></div><div><div class="zim-footer">

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