Table of Contents

Gumpy: A Toolbox Suitable for Hybrid Brain-Computer Interfaces

1.1 Introduction to gumpy

  • Gumpy is a free and open source python software package for brain-computer interfaces
  • Gumpy could be used for EEG and EMG analysis, visualization and decoding
  • Gumpy is suitable for performing online hybrid BCI experiments
  • Gumpy implements advanced deep learning techniques for EEG/EMG decoding
  • Gumpy's source code is released under the MIT license

1.2 Installation

  • Install python : Gumpy only supports python 3
  • Install python interpreter : Gumpy's developers recommend Anaconda python 3 distribution available for Linux, Windows and OSX
  • Install gumpy from github :$ git clone and include the local path where the software is saved to your script as follows: sys.path.append('../../../gumpy')
  • To check your installation: >>> import gumpy

2 Quickstart: Gumpy's modules

  • Gumpy implements different modules

2.1 Dataset module

  • The dataset module allows gumpy's users to read Graz 2b, our recorded EEG and EMG dataset
  • With dataset module, you can read new dataset by subclassing from gumpy.dataset.Dataset
  • data_name =, subject);
    #  Finally, load the dataset 
    • This is a commodity function that allows quick inspection of the data
    • data_name.print_stats();

      2.2 Signal processing module

      • The signal processing module allows gumpy's users to process EEG and EMG signals
      • The signal processing module implements a wide range of functions
      •  #  Filter the data between 2 and 30 Hz 
        filtered_data = gumpy.signal.butter_bandpass(data_name, lo=2, hi=30);
        #  Normalize the data 
        norm_data = gumpy.signal.normalize(data_name, 'mean_std');

        2.3 Plotting module

        • The plotting module implements a wide range of functions for data visualization
        •  #  For example, gumpy implements a function to plot the discrete wavelet approximations and details 
          gumpy.plot.dwt([approximation_C3, approximation_C4], [details_c3_c1, details_c4_c1],['C3, c1', 'C4, c1'], level, data_name.sampling_freq, 'Class: Left');
          #  gumpy also wraps 3D plotting of features into a single line 
           gumpy.plot.PCA("3D", features, X_train, Y_train);

          2.4 Validation module

          • The validation module incorporates many functions for data split
          •  #   Normal split into training and test data 
            gumpy.validation.split(X, labels, test_size);
            #   Cross validation 
            gumpy.validation.cross_validation (X, labels, K);
            #   Time series split 
            gumpy.validation.variable_cross_validation(features, labels, n_splits);

            2.5 Feature extraction module

            • The feature extraction module implements several methods for extracting features from EMG and EEG signals
            •  #  For example, this is a function that performs a feature selection 
              feature_idx, cv_scores, algorithm = gumpy.features.sequential_feature_selector(features, labels, 'classifier_name', number_of_features, Kfold, 'SFFS');

              2.6 Classification module

              • The classification implements several machine learning classifiers : SVM, LDA, KNN, LDA with shrinkage, MLP, Random Forest, Logistic regression Quadratic LDA
              •  #  This is an example how to use gumpy SVM classifier 
                results, clf = gumpy.classify('SVM', X_train, Y_train, X_test, Y_test);
                • The classification module implements a voting classifier using either hard or soft vote and with or without feature selection
                •  #  Voting classifier without feature selection 
                  results, clf =, y_train, X_test, y_test, voting_type, feature_selection=False, k_features);

                  2.7 Deep learning module

                  • The deep learning implements different deep learning models such as vanilla RNN, CNN-STFT, deep & shallow CNN, LSTM and RCNN
                  • To install the gumpy-deep learning module from github :$ git clone

                3 Examples

                3.1 EEG motor imagery

                • This is a jupyter notebook example for EEG motor imagery classification using graz dataset 2b
                • EEG-motor-imagery


                  Append to path and import

                  In case gumpy is not installed as package, you may have to specify the path to the gumpy directory

                  In [20]:
                  %matplotlib inline
                  import sys, os, os.path
                  Once deleted, variables cannot be recovered. Proceed (y/[n])? y

                  import gumpy

                  This may take a while, as gumpy as several dependencies that will be loaded automatically

                  In [21]:
                  import numpy as np
                  import gumpy

                  Import data

                  To import data, you have to specify the directory in which your data is stored in. For the example given here, the data is in the subfolder ../EEG-Data/Graz_data/data. Then, one of the classes that subclass from dataset can be used to load the data. In the example, we will use the GrazB dataset, for which gumpy already includes a corresponding class. If you have different data, simply subclass from gumpy.dataset.Dataset.

                  In [22]:
                  # First specify the location of the data and some 
                  # identifier that is exposed by the dataset (e.g. subject)
                  data_base_dir = '../../../Data'
                  grazb_base_dir = os.path.join(data_base_dir, 'Graz')
                  subject = 'B04'
                  # The next line first initializes the data structure. 
                  # Note that this does not yet load the data! In custom implementations
                  # of a dataset, this should be used to prepare file transfers, 
                  # for instance check if all files are available, etc.
                  grazb_data =, subject)
                  # Finally, load the dataset
                  < at 0x7f7afcff0c18>

                  The abstract class allows to print some information about the contained data. This is a commodity function that allows quick inspection of the data as long as all necessary fields are provided in the subclassed variant.

                  In [23]:
                  Data identification: GrazB-B04
                  EEG-data shape: (1769628, 3)
                  Trials data shape:  (399,)
                  Labels shape:  (399,)
                  Total length of single trial:  8
                  Sampling frequency of EEG data: 250
                  Interval for motor imagery in trial:  [4, 7]
                  Classes possible:  [0 1]

                  Postprocess data

                  Usually it is necessary to postprocess the raw data before you can properly use it. gumpy provides several methods to easily do so, or provides implementations that can be adapted to your needs.

                  Most methods internally use other Python toolkits, for instance sklearn, which is heavily used throughout gumpy. Thereby, it is easy to extend gumpy with custom filters. In addition, we expect users to have to manipulate the raw data directly as shown in the following example.

                  Common average re-referencing the data to Cz

                  Some data is required to be re-referenced to a certain electrode. Because this may depend on your dataset, there is no common function provided by gumpy to do so. However and if sub-classed according to the documentation, you can access the raw-data directly as in the following example.

                  In [24]:
                  if 0:
                      grazb_data.raw_data[:, 0] -= 2 * grazb_data.raw_data[:, 1]
                      grazb_data.raw_data[:, 2] -= 2 * grazb_data.raw_data[:, 2]

                  Example: Notch and Band-Pass Filters

                  gumpy ships with several filters already implemented. They accept either raw data to be filtered, or a subclass of Dataset. In the latter case, gumpy will automatically convert all channels using parameters extracted from the dataset.

                  In [25]:
                  # this returns a butter-bandpass filtered version of the entire dataset
                  btr_data = gumpy.signal.butter_bandpass(grazb_data, lo=2, hi=60)
                  # it is also possible to use filters on individual electrodes using 
                  # the .raw_data field of a dataset. The example here will remove a certain
                  # from a single electrode using a Notch filter. This example also demonstrates
                  # that parameters will be forwarded to the internal call to the filter, in this
                  # case the scipy implementation iirnotch (Note that iirnotch is only available
                  # in recent versions of scipy, and thus disabled in this example by default)
                  # frequency to be removed from the signal
                  if False:
                      f0 = 50.0 
                      # quality factor
                      Q = 50.0  
                      # get the cutoff frequency
                      w0 = f0/(grazb_data.sampling_freq/2) 
                      # apply the notch filter
                      notch_data = gumpy.signal.notch(grazb_data.raw_data[:, 0], w0, Q)


                  Many datasets require normalization. gumpy provides functions to compute normalization either using a mean computation or via min/max computation. As with the filters, this function accepts either an instance of Dataset, or raw_data. In fact, it can be used for postprocessing any row-wise data in a numpy matrix.

                  In [26]:
                  # normalize the data first
                  norm_data = gumpy.signal.normalize(grazb_data, 'mean_std')
                  # let's see some statistics
                  print("""Normalized Data:
                    Mean    = {:.3f}
                    Min     = {:.3f}
                    Max     = {:.3f}
                    Std.Dev = {:.3f}""".format(
                  Normalized Data:
                    Mean    = -0.000
                    Min     = -21.896
                    Max     = 12.008
                    Std.Dev = 1.000

                  Plotting and Feature Extraction

                  Certainly you wish to plot results. gumpy provides several functions that show how to implement visualizations. For this purpose it heavily relies on matplotlib, pandas, and seaborn. The following examples will show several of the implemented signal processing methods as well as their corresponding plotting functions. Moreover, the examples will show you how to extract features

                  That said, let's start with a simple visualization where we access the filtered data from above to show you how to access the data and plot it.

                  In [27]:
                  %matplotlib inline
                  import matplotlib.pyplot as plt 
                  # Plot after filtering with a butter bandpass (ignore normalization)
                  plt.plot(btr_data[grazb_data.trials[0]: grazb_data.trials[1], 0], label='C3')
                  plt.plot(btr_data[grazb_data.trials[0]: grazb_data.trials[1], 1], alpha=0.7, label='C4')
                  plt.plot(btr_data[grazb_data.trials[0]: grazb_data.trials[1], 2], alpha=0.7, label='Cz')
                  plt.title(" Filtered Data")
                  <matplotlib.text.Text at 0x7f7afed88e48>

                  EEG band visualization

                  Using gumpy's filters and the provided method, it is easy to filter and subsequently plot the EEG bands of a trial.

                  In [28]:
                  # determine the trial that we wish to plot
                  n_trial = 120
                  # now specify the alpha and beta cutoff frequencies
                  lo_a, lo_b = 7, 16
                  hi_a, hi_b = 13, 24
                  # first step is to filter the data
                  flt_a = gumpy.signal.butter_bandpass(grazb_data, lo=lo_a, hi=hi_a)
                  flt_b = gumpy.signal.butter_bandpass(grazb_data, lo=lo_b, hi=hi_b)
                  # finally we can visualize the data
                  gumpy.plot.EEG_bandwave_visualizer(grazb_data, flt_a, n_trial, lo_a, hi_a)
                  gumpy.plot.EEG_bandwave_visualizer(grazb_data, flt_b, n_trial, lo_a, hi_a)

                  Extract trials

                  Now we wish to extract the trials from the data. This operation may heavily depend on your dataset, and thus we cannot guarantee that the function works for your specific dataset. However, the used function gumpy.utils.extract_trials can be used as a guideline how to extract the trials you wish to examine.

                  In [29]:
                  # retrieve the trials from the filtered data. This requires that the function
                  # knows the number of trials, labels, etc. when only passed a (filtered) data matrix
                  trials = grazb_data.trials
                  labels = grazb_data.labels
                  sampling_freq = grazb_data.sampling_freq
                  data_class_a = gumpy.utils.extract_trials(flt_a, trials=trials, labels=labels, sampling_freq=sampling_freq)
                  # it is also possible to pass an instance of Dataset and filtered data.
                  # gumpy will then infer all necessary details from the dataset
                  data_class_b = gumpy.utils.extract_trials(grazb_data, flt_b)
                  # similar to other functions, this one allows to pass an entire instance of Dataset
                  # to operate on the raw data
                  data_class1 = gumpy.utils.extract_trials(grazb_data)

                  Visualize the classes

                  Given the extracted trials from above, we can proceed to visualize the average power of a class. Again, this depends on the specific data and thus you may have to adapt the function accordingly.

                  In [30]:
                  # specify some cutoff values for the visualization
                  lowcut_a, highcut_a = 14, 30
                  # and also an interval to display
                  interval_a = [0, 8]
                  # visualize logarithmic power?
                  logarithmic_power = False
                  # visualize the extracted trial from above
                  gumpy.plot.average_power(data_class_a, lowcut_a, highcut_a, interval_a, grazb_data.sampling_freq, logarithmic_power)

                  Wavelet transform

                  gumpy relies on pywt to compute wavelet transforms. Furthermore, it contains convenience functions to visualize the results of the discrete wavelet transform as shown in the example below for the Graz dataset and the classes extracted above.

                  In [31]:
                  # As with most functions, you can pass arguments to a 
                  # gumpy function that will be forwarded to the backend.
                  # In this example the decomposition levels are mandatory, and the 
                  # mother wavelet that should be passed is optional
                  level = 6
                  wavelet = 'db4'
                  # now we can retrieve the dwt for the different channels
                  mean_coeff_ch0_c1 = gumpy.signal.dwt(data_class1[0], level=level, wavelet=wavelet)
                  mean_coeff_ch1_c1 = gumpy.signal.dwt(data_class1[1], level=level, wavelet=wavelet)
                  mean_coeff_ch0_c2 = gumpy.signal.dwt(data_class1[3], level=level, wavelet=wavelet)
                  mean_coeff_ch1_c2 = gumpy.signal.dwt(data_class1[4], level=level, wavelet=wavelet)
                  # gumpy's signal.dwt function returns the approximation of the 
                  # coefficients as first result, and all the coefficient details as list
                  # as second return value (this is contrast to the backend, which returns
                  # the entire set of coefficients as a single list)
                  approximation_C3 = mean_coeff_ch0_c2[0]
                  approximation_C4 = mean_coeff_ch1_c2[0]
                  # as mentioned in the comment above, the list of details are in the second
                  # return value of gumpy.signal.dwt. Here we save them to additional variables
                  # to improve clarity
                  details_c3_c1 = mean_coeff_ch0_c1[1]
                  details_c4_c1 = mean_coeff_ch1_c1[1]
                  details_c3_c2 = mean_coeff_ch0_c2[1]
                  details_c4_c2 = mean_coeff_ch1_c2[1]
                  # gumpy exhibits a function to plot the dwt results. You must pass three lists,
                  # i.e. the labels of the data, the approximations, as well as the detailed coeffs,
                  # so that gumpy can automatically generate appropriate titles and labels.
                  # you can pass an additional class string that will be incorporated into the title.
                  # the function returns a matplotlib axis object in case you want to further
                  # customize the plot.
                      [approximation_C3, approximation_C4],
                      [details_c3_c1, details_c4_c1],
                      ['C3, c1', 'C4, c1'],
                      level, grazb_data.sampling_freq, 'Class: Left')
                  array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f7afebc93c8>,
                         <matplotlib.axes._subplots.AxesSubplot object at 0x7f7afd14d048>,
                         <matplotlib.axes._subplots.AxesSubplot object at 0x7f7afcb0db38>,
                         <matplotlib.axes._subplots.AxesSubplot object at 0x7f7afcf66e80>,
                         <matplotlib.axes._subplots.AxesSubplot object at 0x7f7afcf5e710>,
                         <matplotlib.axes._subplots.AxesSubplot object at 0x7f7afcf6f630>,
                         <matplotlib.axes._subplots.AxesSubplot object at 0x7f7afedef828>], dtype=object)