The P wave is a positive deflection in the human event-related potential. It is most commonly elicited in an "oddball" paradigm when a subject detects an occasional "target" stimulus in a regular train of standard stimuli.
The P wave only occurs if the subject is actively engaged in the task of detecting the targets. Its amplitude varies with the improbability of the targets. Its latency varies with the difficulty of discriminating the target stimulus from the standard stimuli. A typical peak latency when a young adult subject makes a simple discrimination is ms. In patients with decreased cognitive ability, the P is smaller and later than in age-matched normal subjects. The intracerebral origin of the P wave is not known and its role in cognition not clearly understood.
The P may have multiple intracerebral generators, with the hippocampus and various association areas of the neocortex all contributing to the scalp-recorded potential. The P wave may represent the transfer of information to consciousness, a process that involves many different regions of the brain.Muse is a wearable brain sensing headband.
The device measures brain activity via 4 electroencephalography EEG sensors. An accompanying mobile app converts the EEG signal into audio feedback that is fed to the user via headphones.
Choosing MUSE: Validation of a Low-Cost, Portable EEG System for ERP Research
The device operates by representing brain waves that correspond to a more relaxed state through the sound of tweeting birds, and higher amounts of brain activity is represented by storm sounds.
It was demonstrated that Muse can be used for ERP research, with the advantage of it being low cost and quick to setup. Specifically, it can easily quantify NPand reward positivity. It is also widely used for a wide variety of other applications ranging from health and wellbeing to scientific and medical research. It is claimed that using the headband helps in reaching a deep relaxed state. Muse is worn over the ears and connects to a companion mobile app via bluetooth.
The use of Muse enables the use of biofeedbackdiffering from a device like Thync that claims to actually alter brainwaves by wearing it. From Wikipedia, the free encyclopedia. August 12, USA Today. Retrieved August 2, Financial Post. Retrieved Jun 23, Retrieved New Atlas. Frontiers in Neuroscience. August Medical Acupuncture. Cham: Springer International Publishing.
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Make sure your device is turned on and run the following code. Once your Muse is connected and streaming data, put it on and run the following code to view the raw EEG data stream.
The numbers on the side of the graph indicate the variance of the signal. Wait until this decreases below 10 for all electrodes before proceeding.
Modify the variables in the following code chunk to define how long you want to run the experiment and the name of the subject and session you are collecting data from. Seat the subject in front of the computer and run the following cell to run a single trial of the experiment. In order to maximise the possibility of success, participants should take the experiment in a quiet environment and do their best to minimize movement that might contaminate the signal.
With their jaw and face relaxed, subjects should focus on the stimuli, mentally noting when they perceive the oddball stimulus. Depending on experimental conditions, this may require as little as one two minute trial or as many as 6.
We recommend repeating the above experiment times before proceeding. Make sure to take breaks, though! Inattention, fatigue, and distraction will decrease the quality of event-related potentials.
Once a suitable data set has been collected, it is now time to analyze the data and see if we can identify the N It provides helpful functions for performing key tasks such as filtering EEG data, rejecting artifacts, and grouping EEG data into chunks epochs. Plotting the power spectral density PSD of our dataset will give us a glimpse at the different frequencies that are present. A very noisy or flat PSD may represent poor signal quality at certain electrodes. This PSD looks good.
You can tell this dataset was collected in Europe because of the 50hz line noise. Thus, we can filter out all frequencies between 1 and 30 hz in order to increase our ability to detect them. This PSD looks great. The AF8 electrode Front right; light green seems to have some noise in the signal, but the TP9 and TP10 electrodes red and black look great. Next, we will chunk epoch the data into segments representing the data ms before to ms after each stimulus.
No baseline correction is needed signal is bandpass filtered and we will reject every epoch where the amplitude of the signal exceeded 75 uV, which should most eye blinks. Finally, we can now analyze our results by averaging the epochs that occured during the different stimuli and looking for differences in the waveform. Here we can see a beautiful negative deflection in the EEG around ms after presentation of Target stimuli. Because of the location of the source of the P signal in the brain, this means that the direction of the P potential is inverted.Released: May 25, View statistics for this project via Libraries.
Tags muse, lsl, eeg, ble, neuroscience. The code relies on pygatt or BlueMuse for BLE communication and works differently on different operating systems. Note: if you run into any issues, first check out out Common Issues and then the Issues section of this repository. Use the -h flag to get a comprehensive list of all commands and options. To connect to a specific Muse you can pass the name of the device as an argument.
Device names can be found on the inside of the left earpiece e. MuseD2 :. You can also directly pass the MAC address of your Muse. This provides the benefit of bypassing the device discovery step and can make connecting to devices quicker and more reliable:. Note: the process running the stream command must be kept alive in order to maintain the LSL stream. These following commands should be run in another terminal or second process.
If the visualization freezes or is laggy, you can also try the alternate version 2 of the viewer. Note: this will require the additional vispy and mne dependencies. The code to perform these experiments is still available, but is now maintained in the EEG Notebooks repository by the NeuroTechX community. If you want to integrate Muse LSL into your own Python project, you can import and use its functions as you would any Python library. Examples are available in the examples folder:.
These data types can be enabled via command line arguments or by passing the correct parameters to the stream function. Note: The record process will only record from one data type at a time. However, multiple terminals or processes can be used to record from multiple data types simultaneously. Lab Streaming Layer or LSL is a system designed to unify the collection of time series data for research experiments.
It has become standard in the field of EEG-based brain-computer interfaces for its ability to make seperate streams of data available on a network with time synchronization and near real-time access. May 25, May 18, May 17, Feb 21, Jan 28, Dec 12, Jul 23, Jun 22, Jun 12, Download the file for your platform.
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It is considered to be an endogenous potential, as its occurrence links not to the physical attributes of a stimulus, but to a person's reaction to it.
More specifically, the P is thought to reflect processes involved in stimulus evaluation or categorization. It is usually elicited using the oddball paradigmin which low-probability target items are mixed with high-probability non-target or "standard" items. The presence, magnitude, topography and timing of this signal are often used as metrics of cognitive function in decision-making processes.
While the neural substrates of this ERP component still remain hazy, the reproducibility and ubiquity of this signal makes it a common choice for psychological tests in both the clinic and laboratory.
Early observations of the P more specifically, the component that would later be named the P3b were reported in the mids.
Inresearchers Chapman and Bragdon  found that ERP responses to visual stimuli differed depending on whether the stimuli had meaning or not. They showed subjects two kinds of visual stimuli: numbers and flashes of light. Subjects viewed these stimuli one at a time in a sequence.
For every two numbers, the subjects were required to make simple decisions, such as telling which of the two numbers was numerically smaller or larger, which came first or second in the sequence, or whether they were equal. When examining evoked potentials to these stimuli i. Chapman and Bragdon speculated that this differential response to the numbers, which came to be known as the P response, resulted from the fact that the numbers were meaningful to the participants, based on the task that they were asked to perform.
InSutton and colleagues published results from two experiments that further explored this late positivity. They presented subjects with either a cue that indicated whether the following stimulus would be a click or a flash, or a cue which required subjects to guess whether the following stimulus would be a click or a flash. They found that when subjects were required to guess what the following stimulus would be, the amplitude of the "late positive complex"  was larger than when they knew what the stimulus would be.
In a second experiment, they presented two cue types. For one cue there was a 2 in 3 chance that the following stimulus would be a click and a 1 in 3 chance that the following stimulus would be a flash. The second cue type had probabilities that were the reverse of the first.
They found that the amplitude of the positive complex was larger in response to the less probable stimuli, or the one that only had a 1 in 3 chance of appearing. Another important finding from these studies is that this late positive complex was observed for both the clicks and flashes, indicating that the physical type of the stimulus auditory or visual did not matter.
The P300 wave of the human event-related potential.
In later studies published inSutton and colleagues had subjects guess whether they would hear one click or two clicks. This shows two important findings: first, that this late positivity occurred when uncertainty about the type of click was resolved, and second, that even an absence of a stimulus would elicit the late positive complex, if said stimulus was relevant to the task. These early studies encouraged the use of ERP methods to study cognition and provided a foundation for the extensive work on the P in the decades that followed.
The P3a has been associated with brain activity related to the engagement of attention especially the orientinginvoluntary shifts to changes in the environmentand the processing of novelty.
Generally speaking, improbable events will elicit a P3b, and the less probable the event, the larger the P3b amplitude. The P3b can also be used to measure how demanding a task is on cognitive workload. Since the initial discovery of the P, research has shown that the P has two subcomponents.
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