What are Neural Oscillations?

What are Neural Oscillations?

Neural oscillations are repetitive, rhythmic synchronized frequency patterns in the central nervous system. They occur during activation of large clusters of neurons, though they can occur with a single neuron as well. Although the raw data obtained from electroencephalograms are formatted as a function of time, neural oscillations are visualized in terms of frequency and measured in units of Hertz (Hz); one cycle per second. As such the neural clusters generate neural oscillations that can be characterized by the frequency range in which they occur:

  • Alpha waves (7.5-12.5 Hz). Alpha waves also contain a subset of waves known as mu waves, which occur in the same frequency range but are correlated to the motor cortex. (Need examples of that these connect to)
  • Beta waves (13-30 Hz) (need examples of what these connect to)
  • Delta waves (1-4 Hz) (need examples of what these connect to)
  • Gamma waves (30-70 Hz) (need examples of what these connect to)
  • Theta waves (4-8 Hz) (Need examples of what these connect to)

Why do we produce neural oscillations?

We do not fully understand why our brain produces neural oscillations. Some researchers theorize that neural oscillations are nothing more than byproducts of brain activity, indicators of expected brain pattern in response to events of stimulus. For example, motion produces prdecitable occurrences of alpha waves (in the mu frequency) in the motor cortex, and sleep cycles are characterized by the alternating flux of different neural waves. Other researchers have theorized that cetain oscillations, like those that occur in the delta range, are critical to unlocking the mystery of our consciousness.

So why do neural oscillations matter?

Neural oscillations are useful in variety of ways. From a diagnostic and imaging persepective, they can be used as indicators of specific neurological phenomena such as:

  • Sleep and the state of consciousness
  • Motor control
  • Perception and information processing
  • Pattern generation
  • Memory
  • Abnormal neural function, such as epilepsy, and Parkinsons.

Practical applications for extracting neural oscillations using your own BCI could be looking at the presence of mu waves during motion, or to check if there is a presence of alpha and beta waves during meditation. You may even want to look at all the different waves that occur when you are performing a specific task.

How do we extract neural oscillations as a feature of our EEG data?

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As previously mentioned the data obtained by EEF is captured as a function of time, but neural oscillations are described in units of frequency. In order to transform the data we must employ a Fourier transform. The Fourier transform is a highly regarded formula which is the mainstay formula for signal processing and signal decompistion. This will display an animated GIF The best way to extract neural oscillations is to perform a Fourier transform on your preprocessed data and then plot the resulting frequency patterns in the category of brain waves your interested in seeing. You can further preprocess your data to exlude certain channels, or target specific frequency ranges to oberseve features of the neural oscillation. >See NeuroTechX.edu "Preprocessing" for a detailed look at the preprocessing steps that that can be applied to your data. Before extracting neural oscillations there are several steps that must be undertaken to prepare you data: * Importing data, reading data, and formatting data * Preprocessing * Epoching * Assemble a classifier (plotting a topomap) * and finally, plotting the relevant figure ### Importing, reading, and formatting data In the below example I have used a dataset created by experimental runs by (reserach reference) so when the data is fetched it will have already underwent some preprocessing which will not be covered in either examples. However in both cases the data will be fetched using the below commands: subject = 1
runs = [3]
tmin = -0.1
tmax = 0.3
raw_fnames = eegbci.load_data(subject,runs)
raw_files = [read_raw_edf(f, preload=True) for f in raw_fnames]
raw = concatenate_raws(raw_files)
raw.ch_names.index('STI 014')
Line 2 is relevant in this example, as there are 14 experimental runs to choose from that were performed in this study and each was tested under different conditions. In this experiemtn run 3 measured the eeg signal obtained during movement of the left and right hands, both speerately and simultaneously. ### Preprocessing When plotting power spectral density (psd), only epoching is necessary as we want to see the psd accross the entire available frequency range. However for topomap plotting you will need to prepocess you data with a band pass filter to isolate the specific frequency range you want to visualize. The example below aslo strips the channel names of their default "." keys to avoid errors when reading your channel names. ### Epoching the data Epoching is basically segmenting your data into smaller chunks of readable data that contain events, or fluctuations in the eeg signal (caused by changing signal potential?). Epoching helps to trunkate the objects you need to analyze into more manageable bytes that contain the information you actually want. To epoch your data you can follow the steps below: events = mne.find_events(raw, stim_channel='STI 014', verbose=True)
picks = pick_types)raw.info, meg=Fale, eeg=True, stim=False, eog=False, exclude='bas')
baseline = 0, None
The above defines the events you want to look for, as well as the type of signal your program should expect to analyze (meg vs. eeg vs. eog). I selected the eeg=True as the data we will be looking at was obtained using an EEG headset. Below we continue the necessary inputs for epoching: epochs = Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks, baseline-baselin, preload=True)
The above code defines the epochs which will be further manipulated. Try the function __plot.epochs()__ to see how your data has transformed! (Hint: It might look something like the image below!) The following steps will diverge depending on the methods of visualization you want to apply. Below I will highlight how to use psd to visualize dominant frequencies as well as topomaps to highlight specific frequencies in localized areas. ### Using PSD to categorize oscillatory occurance based on power spectral content Power spectral density measure the power of a signal in (UNITS). The function __epochs[events].plot_psd()__ can be used to plot specific epoched events as a function of power spectral desnity over a specific frequency range. Below is an example of a script you can run to achieve this. Note that the dataset imported from eegbci is already preprocessed and transformed using the Fourier function, so the steps you should see below are: * Importing, read, and format your data * Epoch the data * Plot the data with the __epochs[events].plot_psd()__ __Figure 1:__ Power spectral density plotted for the electrode C3 located in the left side of the motor cortex. It is evident that the psd content is most evident in the alpha range and theta range, evident of neural oscillations occurring during movement of the right hand. __Figure 2:__ Power spectral density plotted for the electrode C4 located in the left side of the motor cortex. It is evident that the psd content is most evident in the alpha range and theta range, evident of neural oscillations occurring during movement of the left hand. ### Using a topomap to visualize local oscillations A classifier will be applied to visualizethe presence of neural oscillations using a binary discrimination to highlight whether you have the presence of waves at certain frequencies or not. While there is a bit of variability on visualizing the strenght of your signal, this is really simply a "there" or "not there" method of looking at neurons firing over specific electrodes. Understanding which electrodes correspond to which regions of the brain will help you to understand where the majority of activity is occurring in the brain. Plotting PSD to visualize frequency is limited to specific chanels and may require excess work to view the local presence of waves. By contrast, topomap offers specific feedback on the local presence of waves. The classifier assembled in the below example uses a binary range characteried by the red positive values and the blue negative values. The total range is from -0.1 to 1.0 and refers to the electric potential with 1.0 indicating the presence of a measureable signal. There are several more steps involved with topomap visualization. The first occurs in the preprocessing step as you must apply a band pass filter (raw.filter(fmin,fmax)) to isolate the frequency range that interests you. Refer to the above section where I break down each wave into their respective frequency range. The below example will be using a band pass filter for alpha and beta waves isnce the eegbci dataset runs are based on on motor cortex activity. Five arbitrary time points were chosen in the below example to highlight the shift in wave presence over time. Since the run involves squeezing the right and left hands, we should expect an increase in the alpha wave presence (specifically the mu frequency) and a reciprocal decrease in beta wave frequency in the area corresponding to either right or left hand (oposite sides of the brain from the hand involved). Timepoints 4 and 5 illustrate this difference: __Figure 3:__ Topomap of alpha waves (7.5-12.5 Hz) during movement. __Figure 4:__ Topomap of beta waves (13-30 Hz) during movement.