Стимулға байланысты ми жауабы және электроэнцефалография (ЭЭГ)

Brain response that is the direct result of a specific sensory, cognitive, or motor event

An event related potential (ERP) is the measured brain response that is the direct result of a specific sensory, cognitive, or motor event. More formally, it is any stereotyped electrophysiological response to a stimulus. The study of the brain in this way provides a noninvasive means of evaluating brain functioning. ERPs are measured by means of electroencephalography (EEG). The magnetoencephalography (MEG) equivalent of ERP is the ERF, or event related field. Evoked potentials and induced potentials are subtypes of ERPs.

With the discovery of the electroencephalogram (EEG) in 1924, Hans Berger revealed that one could measure the electrical activity of the human brain by placing electrodes 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 cognitive neuroscience because using pure EEG data made it difficult to isolate individual neurocognitive 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 Hallowell Davis recorded the first known ERPs on awake humans and their findings were published a few years later, in 1939. Due to World War II 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 Grey Walter and colleagues began the modern era of ERP component discoveries when they reported the first cognitive ERP component, called the contingent negative variation (CNV). Sutton, Braren, and Zubin (1965) made another advancement with the discovery of the P3 component. 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 cognitive neuroscience research to study the physiological correlates of sensory, perceptual and cognitive activity associated with processing information.

ERP waveforms consist of a series of positive and negative voltage deflections, which are related to a set of underlying components. Though some ERP components are referred to with acronyms (e. g., contingent negative variation – CNV, error related negativity – 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 ordinal 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 N100 (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 P200 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 P300 component may exhibit a peak anywhere between 250 ms – 700 ms.

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.

Unlike microelectrodes, which require an electrode to be inserted into the brain, and PET scans that expose humans to radiation, ERPs use EEG, a non invasive procedure.

ERPs are used extensively in neuroscience, cognitive psychology, cognitive science, and psycho physiological research. Experimental psychologists and neuroscientists 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 transduced visual stimulus to reach the cortex after light first enters the eye. Alternatively, the P300 response occurs at around 300ms in the oddball paradigm, for example, regardless of the type of stimulus presented: visual, tactile, auditory, olfactory, gustatory, 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 salient stimuli. The P300 response has also been studied in the context of information and memory detection. 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. Another area of research in the field of ERP lies in the efference copy. This predictive mechanism plays a central role in for example human verbalization. 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. Other ERPs used frequently in research, especially neurolinguistics research, include the ELAN, the N400, and the P600/SPS. The analysis of ERP data is also increasingly supported by machine learning algorithms.

A common issue in ERP studies is whether the observed data have a sufficient number of trials to support statistical analysis. 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 or estimates of internal consistency to justify the examination of individual differences.