The 28th Annual Conference of the Japanese Neural Network Society (October, 2018)

Empirical mode decomposition for improved EEG signal
classification with Convolutional Neural Network in
Brain-Computer interface experiments
Kahoko Takahashi (P)1 , Zhe Sun2 , Jordi Sol-Casals3 , Andrzej Cichocki4 , Anh Huy Phan4 and Ruggero Micheletto1
1Yokohama City University, Graduate School of Nanobioscience,
22-2 Seto, Kanazawa Ward, Yokohama, Japan
2
Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity,
RIKEN, 2-1 Hirosawa, Wako-shi, Saitama, Japan
3
University of Vic - Central University of Catalonia,
Data and Signal Processing Research Group, Vic, Catalonia (Spain)
4Skolkowo Institute of Science and Technology (SKOLTECH), Moscow, Russia
E-mail: n175224a@yokohama-cu.ac.jp
Abstract— Electroencephalogram (EEG) is generally known as a non-destructive method to examine
the functioning of human brains. These days, the development of Brain Computer Interface (BCI) technology is being actively promoted. In motor imagery (MI)tests, participants were asked to think of moving theleft or right arm during EEG recording. The data wereclassified by analyzing Wavelet Transformed EEG signals using Convolutional Neural Network (CNN). The
quality of this classifier relies on the amount of data
used for training. However, it is difficult and time
consuming to collect enough EEG data because of thelimited availability of subjects. Therefore we created
new artificial frames by applying Empirical Mode Decomposition (EMD) on the EEG frames and mixing
their Intrinsic Mode Function (IMFs)[1]. These artificial frames were used as training data with significantly better classification accuracy.

ing was of 2 seconds with a 4 second break between thetrials.
The dataset was recorded for 2 subjects and has 200
trials for each class (left or right thought movements).
In total, 60 trials (30 trials for each class) were used forthe training process and 140 trials (70 trials for eachclass) were used for the actual classification tests. The
trials were performed in order, the first 30 trials belong
to the left category and the remaining trials are for theright class.
3 Methods
3.1 Convolutional Neuronal Network
In figure 1 we show the structure of the convolutional neural network (CNN) used. The first layer in
the input tensor is of dimension 23x50x62 (the signal
pre-processing is done with wavelets with output dimensions 23x50, 62 is the number of EEG channels).

Keywords— Convolutional Neural Networks, CNN,EEG, Empirical Mode Decomposition, EMD, Wavelets,
Classification Neural Network

1Introduction
Brain computer interfaces (BCI) are of growing interest nowadays. These devices will provide means
to send commands of various complexity to computers and this will help people with handicaps and may
contribute to the realization of security devices in various fields. We are involved in experiments where subjects are requested to think about moving their left orright arm while their EEG pattern is recorded. We
developed a Convolutional Neural Network (CNN) toclassify these data. In this study we improved thenetwork learning process using the so called Empirical
Mode Decomposition[1] and used advanced wavelets
analysis for the classification process. With this novel
approach we could increase the network classification
accuracy significantly.
2Data Set
We used EEG data from motor imagery dataset[2].
These datasets are relative to left/right motor imagery(MI) movements recorded from 62 channels (with sampling frequency of 500 Hz). The duration of the record-Figure 1: A schematic representation of the convolutional neuronal network used.
4Artificial EEG
We used an Empirical Mode Decomposition (EMD)
approach to create the new artificial EEG signals[1].This algorithm decomposes the original signal intoa finite number of functions called IMFs (IntrinsicMode Function). Then the new artificial EEG signals can be created by randomly combining some IMFs
from different real EEG signals. We made the new artificial EEG from 60 real EEG data for each subjects
and used them for the CNN training process.
5Wavelet transform
The EEG signal is a one-dimensional time series of 2
seconds of duration for 1000 points (500Hz sampling).
We used a Morlet wavelet, this transformation converts the time series to a two-dimensional array of dimension 23x50 [2].

Figure 2: Example of dynamic spectra of EEG

Since we have 62 channels the imput tensor is of
23x50x62, see figure 2 for an example of the output.
In the time domain EEG data of each trial can be
represented as a matrix (2-D) of 62 channels 1000
samples (500Hz, 2s). The Morlet motherwavelet usedis the following:1−t2)
ΨM orlet (t) = √exp(2iπfc t) exp(fbπfb(1)

where the parameter fb and fc are 1Hz. The transformation integral is done with equation 2, where thetwo scaling parameter a and b are variable.

Figure 3: Validation loss output of the CNN
network in case of Subject 1 (top) and 2 (bottom). The red line is done with the original
60 training frames, the colored curves are instead output done with an increasing number of artificial frames done with our original
EMD method. The improvement of performance is evident.

Table 1: Classification accuracy with different numberof training data.
Number of training data1WΨ [χ(t)] = √a6Z +∞t−b)dt
χ(t)Ψ(a−∞(2)Results

We used four types of dataset to test the accuracy ofour methodology. First, the 60 original dataset wereused for training our CNN network.
Then, using the EMD and wavelet method describedabove, we constructed 120 artificial dataset framesthat were added to the original 60, for a total of 180
frames for the training. This process was repeated in
the same way for a total of 360, and 660 frames.
As a result, the validation loss (network output error) performance of the network improved and it isplotted for the two subjects in figure 3. For the threegroups of new training data (180,360 and 660) we observed an increasingly faster convergence.
As a consequence, the CNN improved its accuracy
significantly, it was about 84% for the first subject and
81% for the second and reached over 90% in one case.
See table 1 for details the results.

60 (Original Dataset)
180 (120 Artificial Frames)
360 (300 Artificial Frames)
660 (600 Artificial Frames)

Accuracy
Subject 1
84.29%
86.43%
91.43%88.57%

Subject 2
81.43%
85.71%
87.86%
87.86%

References[1] Dinaers K. A New Method to Generate Artificial
Frames Using the Empirical Mode Decomposition
for an EEG-Based Motor Imagery BCI. Frontiers
Neuroscience. 2008;22.
[2] Phan AH, Cichocki A.
Tensor decompositions for feature extraction and classification ofhigh dimensional datasets.
Nonlinear Theory
and Its Applications, IEICE. 2010;1(1):37–68.
doi:10.1587/nolta.1.37.