Blending CNNs with Different Signal Lengths for Real-time EEGClassification Sensitive to the Changes
Takashi Ota and Kenji Funahashi
1Abstract— Although a lot of BMI research has been performed,
CNN's response to changes in the input EEG is too late to proceed inreal-time. We propose a method to improve the real-time
performance by blending multiple CNNs with different input
lengths.

I. INTRODUCTION
Brain Machine Interface (BMI) is a technology that uses brain
information such as electroencephalogram (EEG) to connect abrain to machines. EEG is the electrical activity generated in thebrain, and is recorded non-invasively. EEG measurement devices
that are small, portable, and have temporal resolution are available.
Therefore, a lot of research on brain machine interface using EEG
has been actively conducted. Especially in recent years, there has
been a lot of research on EEG classification using neural networks.
For example, Schirmeister et al. showed that Convolutional
Neural Network (CNN) can classify EEG with the same accuracy
as Filter Bank Common Spatial Pattern (FBCSP)[1], aconventional EEG decoding technique[2]. Although CNN ishighly accurate and selects features automatically, it requires acertain length of signal input in the time axis direction to improveaccuracy. That will be a delay for real-time system. In this paper,
we propose a new method to blend CNNs with different input
signal lengths, and verified its usefulness.

II. PROBLEMS
EEG classification by CNN involves inputting EEG intensity
data on a two-dimensional plane with the horizontal axis being thetime axis and the vertical axis being the electrode number into aCNN, and obtaining the output of the class with the highest
estimated probability. When the input signal length to the CNN islong in the time axis, there is a disadvantage that the tracking ofthe change in the user's imaged class is delayed. In other words,
right after the user’s imaged class changes, the input to the CNN
contains almost no EEG of post-change imaged class (Figure.
1-(a)), and the estimated probability of the pre-change imaged
class (Class A) becomes high. When the EEG of the post-change
imaged class becomes the majority of the input to the CNN(Figure. 1-(b)), the prediction probability of the post-change
imaged class (Class B) becomes high for the first time. One of theCNN architectures used in research[2] had an input data of 522
samples in the time direction for a 250Hz sampling EEG. If thisCNN is used for the real-time system, EEG of post-change
imaged class occupies all of the input of the CNN, barely522⁄255 ≒ 2.1 seconds after the imaged class changes.

III. PROPOSED METHOD AND CONCLUSION
We propose a method to blend CNNs with different input
signal lengths in the time axis direction. By blending CNNs withdifferent input signal lengths, classifier that is sensitive to EEGchanges, and has high classification performance under normal
conditions can be obtained. In other words, this method enables aclassifier that combines the advantages of both CNNs with short(a) Right after the change of imaged class(b) When all input signals are in the post-change imaged class
Figure.1: Input signals after imaged class changes
input signal length and CNNs with long input signal length. In
this study, we generated multiple CNN classifiers for threedifferent input signal lengths using 256 Hz sampling EEG data,each with a different random seed. Then, we compared theblended CNNs with different input signal lengths (proposedmethod) with the blended CNNs with the same input signal length.
The results showed that the proposed method outperformed theblended classifier with medium input signal length in terms ofclassification stability and response speed to changes in theimaged class. Its delay to the change in imaged class also
decreased to 33% of the blended classifier with long input signallength, although it was inferior in terms of classification stability.

IV. ACKNOWLEDGEMENTThis work was supported in part by JSPS KAKENHI Grant
Number JP20K11918.

REFERENCES[1] Ang KK, et al.: Filter Bank Common Spatial Pattern (FBCSP) in
Brain-Computer Interface, IEEE International Joint Conference on Neural
Networks 2008, IJCNN, pp. 2390–2397, 2008.
[2] Schirrmeister RT, et al.: Deep Learning With Convolutional Neural
Networks for EEG Decoding and Visualization, Human Brain Mapping,Vol. 38, No. 11, pp. 5391–5420, 2017.