11institutetext: Computer Science and Engineering,22institutetext: Centre for
Cognitive and Brain Sciences, IIT Gandhinagar
22email: pankaj.p<EMAIL_ADDRESS>
# BRAIN2DEPTH: Lightweight CNN Model for Classification of Cognitive States
from EEG Recordings

Pankaj Pandey 11 Krishna Prasad Miyapuram 22

###### Abstract

Several Convolutional Deep Learning models have been proposed to classify the
cognitive states utilizing several neuro-imaging domains. These models have
achieved significant results, but they are heavily designed with millions of
parameters, which increases train and test time, making the model complex and
less suitable for real-time analysis. This paper proposes a simple,
lightweight CNN model to classify cognitive states from Electroencephalograph
(EEG) recordings. We develop a novel pipeline to learn distinct cognitive
representation consisting of two stages. The first stage is to generate the 2D
spectral images from neural time series signals in a particular frequency
band. Images are generated to preserve the relationship between the
neighboring electrodes and the spectral property of the cognitive events. The
second is to develop a time-efficient, computationally less loaded, and high-
performing model. We design a network containing 4 blocks and major components
include standard and depth-wise convolution for increasing the performance and
followed by separable convolution to decrease the number of parameters which
maintains the tradeoff between time and performance. We experiment on open
access EEG meditation dataset comprising expert, nonexpert meditative, and
control states. We compare performance with six commonly used machine learning
classifiers and four state of the art deep learning models. We attain
comparable performance utilizing less than 4% of the parameters of other
models. This model can be employed in a real-time computation environment such
as neurofeedback.

###### Keywords:

EEG CNN Deep Learning Meditation NeuroFeedback Neural Signals

##  1 Introduction

Deep Learning (DL) has sparked a lot of interest in recent years among various
research fields. The most developed algorithm, among several deep learning
methods, is the Convolutional Neural network (CNN) [12, 32]. CNNs have made a
revolutionary impact on computer vision, speech recognition, and medical
imaging to solve challenging problems that were earlier difficult using
traditional techniques. One of the complex problems was classification. In the
short span of 4 years from 2012 to 2015, the ImageNet image-recognition
challenge, which includes 1000 different classes in 1.2 million images, has
consistently shown reduced error rates from 26% to below 4% using CNN as the
major component [21]. Identification of brain activity using CNNs has
established remarkable performance in several brain imaging datasets,
including functional MRI, EEG, and MEG. For example, Payan and Montana
classified Alzheimer’s and healthy brains with 95% accuracy using 3D
convolution layers on the ADNI public dataset containing 2265 MRI scans [25].
In the recent work, Dhananjay and colleagues implemented three layers of CNN
architecture to predict the song from EEG brain responses with 84% accuracy,
despite having several challenges such as having low SNR, complex naturalistic
music, and human perceptual differences [30]. However, all these models are
building complex and deep networks without considering the limitation of time
and resources. Two main concerns observed with deep and wide architectures are
having the millions of parameters that lead to high computational cost and
memory requirement. Therefore, this opens up the opportunity to navigate the
research to develop lightweight domain-specific architectures for resource and
time constraint environments. One such scenario is a real-time analysis of EEG
signals.

EEG brain recordings have a high temporal resolution as well as a wide variety
of challenges such as low signal-to-noise ratio, noise can be of different
shape, for example, artifacts from eye movement, head movement, and electrical
line noise [20]. Hence, to extract significant features, it requires a
sophisticated and efficient method that considers the spatial information in
depth. In recent times, deep learning methods have been showing a significant
improvement over traditional machine learning algorithms. And, DL models have
been expanding in real-time computation also. Real-time analysis requires fast
train as well as test time. The major property that should hold in lightweight
architecture is to design the blocks, which reduces trainable parameters while
maintaining state-of-the-art performance [31]. A recent paper[30] on the
classification of EEG signals has introduced a significant performance by
generating the time-frequency images from EEG signals, but the proposed model
is made of several components, which loads the model with 5.8 million
parameters. In this study, we have proposed a novel pipeline to generate 2D
images from EEG signals and develop a model that produces comparable
performance with state-of-the-art networks with minimum time for training and
testing and suitable for EEG classification tasks.

Figure 1: NeuroFeedback protocol: EEG signals are passed to the neural
computational toolbox, including preprocessing and analysis stages. Analysis
pipeline comprises required computational algorithms, which generate feedback
for the participant in real-time with minimum lag.

##  2 Related Studies

###  2.1 Cognitive Relevance

Meditation is a mental practice that enhances several cognitive abilities such
as enhancing attention, minimizing mind wandering, and developing sustained
attention [7]. EEG is the most widely used technique in the neuroscientific
study of meditation. EEG signals are decomposed into five frequency bands,
including delta (1-4Hz), theta (5-8 Hz), alpha (8-12 Hz), beta (13-30 Hz), and
gamma (30-70 Hz). Several meditation studies report the importance of theta
and alpha waves for enhancing attention and are associated with several
cognitive processes [5]. These findings led the researchers to design a
neurofeedback protocol [6] to modulate oscillatory activities on the naive
participants; a mechanism derives from the focused-attention meditation
technique. Neurofeedback is a process to provide visual/audio feedback to a
participant while recording his/her neural responses in real-time, which
develops skills to self-regulate electrical activity of the brain, as shown in
Fig.1. Previous studies on neurofeedback have shown promising results to
enhance performance, including athletes (archers improve their shooting
performance), musicians, professional dancers, and non-artists to attain
skills resembling visual artists [27]. NeuroFeedback requires a time-efficient
and high-performing computational technique in terms to classify the different
stages of brain responses.

###  2.2 Deep Learning Networks

There are four state-of-the-art architectures that have been the best way to
understand the significance of performance and light-weight architecture.

Deep CNN Architectures: Deep CNNs (DCCN) have been introduced to generate
local low-level and mid-level feature learning in the initial layers and high-
level and global learning in the deeper layers. High-level features are the
combination of low and mid-level features. VGG is one of those DCNNs that
decreased the ImageNet error rate from 16% to 7% [29]. VGG expresses Visual
Geometry Group. They employed small convolution filters of size 3$\times$3 in
place of the large filters comprising 11$\times$11, 5$\times$5 filters and
observed the significant performance with varying depth of the network. The
use of small filters also reduced the no of parameters, thus decreasing the
number of computations. ResNet50 is the next powerful and successful network
after VGG, which stands for residual networks having 50 layers [15]. ResNet
was introduced to address two problems. The network has the ability to bypass
a layer if the present layer could decrease performance and leads to
overfitting, and this process is referred to as identity mapping. Another
significance is to allow an alternate shortcut path for the gradient to flow
that could avoid vanishing gradients problem [12]. ResNet decreased the error
rate of 7%(VGG) to 3.6%.

Lightweight CNN Architectures: MobileNet V1 was an early attempt to introduce
the lightweight model [16]. The remarkable performance of this model was
achieved by substituting the standard convolution operation with depthwise
separable convolution. This enhances the feature representation, which makes
the learning more efficient. The two primary components of depthwise separable
convolution are depthwise and pointwise convolution, which are introduced to
adjust channels and reduce parameters [18]. The stacking of these two
components generates novelty in the model. After this, another model proposed
was Mobilenet V2 [26]. Mobilenet V2 is the extension of Mobilenet V1. Sandler
and colleagues identified that non-linear mapping in lower-dimension increased
information loss. To address this problem, a significant module was introduced
with three consecutive operations. Initially, the dimension of feature maps is
expanded using 1$\times$1 convolution, followed by, a depthwise convolution of
3$\times$3 to retain the abstract information. And in the last part, all the
channels are condensed into a definite size using 1$\times$1 pointwise
convolution. These transformations are processed into a bottleneck residual
block which is the core processing unit in place of standard convolution.

In our study, we use standard and depthwise convolution to enhance the
performance and depthwise separable convolution to make the model time-
efficient. The novelty of our work lies in the proposed pipeline to develop 2D
plots from EEG signals having 3 RGB channels representing power spectral,
which preserves the oscillatory information along with spatial position of
electrodes. We classify three cognitive states comprising expert, non-expert
meditative states, and control states (no prior experience of meditation).
This paper discusses the following components a) Our proposed pipeline b)
Experimentation on Dataset c) Comparative studies of ML and DL models d)
Ablation Study

##  3 Data and Methods

###  3.1 Data and Preprocessing

We used two open-access repositories consisting of Himalayan yoga meditators
and controls [4, 3]. In this research, we used EEG data of 24 meditators and
12 control subjects. Twenty-four meditators were further divided into two
groups comprising twelve experts and twelve non-experts. Data were captured
using 64 channels Biosemi EEG system at the Meditation Research Institute
(MRI) in Rishikesh, India. Experimental design and complete description are
mentioned in the paper [5]. The expert group had an experience of a minimum of
2 hours of daily meditation for one year or longer, whereas non-experts were
irregular in their practice. Control subjects had no prior meditation
experience and were asked to pay attention to the breath’s sensations,
including inhalation and exhalation. A recent study [24] has shown the
significant differences between expert and non-expert meditators and refers to
as two distinct meditative states. Here, we refer to meditative and control
states as cognitive states. As it includes cognitive components, such as
involvement of attention during inhalation and exhalation of breathing,
practitioners engaging in mantra meditation which enhances elements of
sustained attention and language[5, 22, 2]. EEG data corresponding to
breathing and meditation were extracted and preprocessed using Matlab, and
EEGLAB software [11]. We classified three states emerging from expert, non-
expert, and control groups, respectively.

EEG signals were downsampled at 256 Hz. A high pass linear FIR filter of 1 Hz
was applied followed by removing the line noise artifacts at frequencies of
50,100,150,200, 250. Artifact correction and removal were performed using
Artifact Subspace Reconstruction (ASR) method. Bad channels were removed and
spherical interpolation was performed for reconstructing the removed signal,
an essential step to retain the required signal. Data were re-referenced to
average. Independent Components Analysis (ICA) was applied to classify the
brain components and to remove the artifacts, including eye blink, muscle
movement, signals generated from the heart, and other non-biological sources.

###  3.2 Methods

We divide the classification pipeline into two processing units. The first is
to create power spectral density 2D plots from neural responses, known as
“Neural Timeseries to 2D” and the second is to define the classification model
term as “2D to Prediction” as shown in Fig. 2.

  1. 1.

Neural Timeseries to 2D: We divide the process of creating images from EEG
signals into the following three steps.

    1. (a)

_Window Extraction_ : EEG time-series signals are extracted into windows of 2,
4, and 6 seconds. For example, if a signal contains 24 seconds, we get 12, 6,
and, 4 no of windows respectively. Varying window length identifies the
information content and plays a significant role in discriminating the
classes. In some applications, we have the luxury to extract varying windows
sizes depending on the task.

    2. (b)

_Power Spectral Analysis_ : Power spectral density (PSD) is estimated for the
extracted window using the Welch method [13]. Welch’s method is also known as
the periodogram method for computing power spectrum, the time signal is
divided into successive blocks followed by creating a periodogram for each
block, and estimating the density by averaging. Oscillatory cortical activity
related to meditation primarily observes in two frequency bands, theta (5-8Hz)
and alpha (9-12Hz) [5]. These bands are further subdivided into
theta1(5-6Hz),theta2(7-8 Hz), and alpha1(9-10Hz), alpha2(11-12Hz). For every
channel, PSD is computed for the mentioned four bands.

    3. (c)

_Topographic (2D- 3 Channel) Plot_ : We use the topoplot function of the
EEGLAB that transforms the 3D projection of electrodes in a 2-D circular view
using interpolation on a fine cartesian grid [14]. Topographic plots were
earlier implemented in Bashivan’s work [1], and they combined plots from three
bands to form one image of three channels, whereas we create one image of size
32 $\times$ 32 for each band having three RGB channels, and this might help to
understand the significance of each band in the specific cognitive task. This
plot preserves the relative distance between the neighboring electrodes and
their underlying interaction, generating task-based latent representation
using convolution.

(a)Neural TimeSeries Signals to 2D: Images are formed using power spectral
analysis.

(b) 2D to Prediction: Images are fed into a deep learning network comprising
of 4 blocks.

Figure 2: Proposed pipeline for classification

  2. 2.

2D to Prediction: Our model comprises 4 Blocks as shown in Fig. 2 (b). The
first two blocks are introduced to capture deep feature information and the
third block for reducing the computation. The major components in these three
blocks are regular Conv2D, depthwise spatial Conv2D, depthwise separable
convolution, max pooling, ReLu(Rectified Linear Unit) activation function, and
batch normalization.

Conv2D to learn richer features at every spatial scale. Depthwise convolution
which acts on each input channel separately and extremely efficient to
generate succinct representation [9]. Depthwise separable convolutions
factorize a regular convolution into a depthwise convolution and a (1,1)
convolution called a pointwise convolution [17]. This was initially introduced
for generic object classification [28] and later used in Inception models [19]
to reduce the computation in the first few layers. Kernel sizes for the
initial two blocks are 3$\times$3 and 2$\times$2 for standard 2D convolution
with 64 filters and 2$\times$2 for Depthwise convolution. The third block
comprises standard Conv2D and depthwise separable convolution of kernel size
2$\times$2 with 64 and 12 filters respectively. ReLu activation is introduced
in the initial two layers to prepare the network to learn complex
representations and to generate faster convergence and better efficiency [23].
The output of ReLu is processed by the Max Pooling operations for spatial sub-
sampling, which downsample the feature maps by generating features in patches
of the feature map [9]. Batch normalization is performed at last in the three
blocks to minimize internal covariate shift, which subsequently accelerates
the training of deep neural nets and enables higher learning rates [19].

Fourth block contains flatten, two dense layers and a softmax activation
function. A dense layer is employed to combine all the features learned from
previous layers where every input weight is directly connected to the output
of the next layer in a feed-forward fashion. Since we are doing multiclass
classification, the output layer has a softmax activation function. Softmax as
an activation function is used because the model requirement is to predict
only the specific class, which results in high probability. To find out the
loss of the model, categorical cross-entropy is used to predict the
probability of a class.

Deep learning models are trained using tensorflow(keras) [10] and machine
learning algorithms employ scikit learn library [8]. GPU NVIDIA GTX 1050(4GB
RAM) are used for this study and the batch size are set to 30 because of
memory constraints and kept the maximum epoch to 30 to maintain the timing
constraint as well as to avoid overfitting and for optimal training and
validation loss.

##  4 Results and Discussion

This section discusses five measures a) performance of our model b) comparison
of time and parameters c) training and validation loss d)visualization of
layers e) ablation study

###  4.1 Performance

Baseline Methods: We compared the performance with commonly used classifiers
and state of the art deep learning models. We trained six machine learning
classifiers and tried several hyperparameters as shown in Table 1 and reported
the maximum accuracy for all except NLSVM because it was around chance level.
We experimented with four state of the art deep learning models VGG16[29],
ResNet50[15], MobileNet[16], MobileNetv2[26] and keep block4 of our model as
the last block in all the models. These models are best suited for
classification tasks. We reported the cross-subject average accuracy using
leave one out validation, eleven subjects from each condition were used for
training and one subject from each condition was used for testing and then
reported the average accuracy of 12 iterations. For example, in the 2-sec
window, 9909 samples were used for training and 1101 for validation and 813
samples for testing, this was iterated for twelve times.

Table 1: Machine Learning Classifiers

Classifier | Parameters
---|---
| Linear SVM
---
(SVM)
penalty parameter C values: 0.1,0.5,1.5,5,20,40,80,120,150
| Non Linear
---
SVM (NLSVM)
| kernel: ’rbf’, upper bound on the fraction of margin errors
---
nu : 0.3,0.4,0.5, 0.6
AdaBoost (AB) |  | algorithm = SAMME.R, no of estimators : 50, 100,200,250,
---
300,400
| Logistic
---
Regression (LR)
regularisation : l1, solver: saga
| Linear
---
Discriminant
Analysis(LDA)
threshold rank estimation = 0.0001
| Random
---
Forest( RF)
| no of estimators: 50,100,150,200,250,300,400, min samples
---
leaf:5, criterion: entropy

Figure 3: Performance of ML Models: Classification of three cognitive states;
expert, non-expert meditative states, and control. Each box indicates five
accuracy values representing classifiers mentioned in the right top corner.
Bold text represents the maximum value in the box. Figure 4: Performance of
CNNs: Each column of a box represents accuracy, precision, recall, and
f1-score, and row indicates the CNN network mentioned on the right top corner.

We obtained maximum accuracy 64.3% in theta1, 60.8% in theta2, 56.5% in
alpha1, and 56.1% in alpha2 for a 2-sec window using Random Forest Classifier
as shown in Fig. 3. Deep learning models completely outperformed traditional
ML techniques, we obtained 97.27% in VGG16, 91.01% in ResNet50, 90.75% in
MobileNet, 88.73% in MobilenetV2 and 94.57% in Brain2Depth(Ours) for theta1 in
2sec window as shown in Fig. 4. The minimum accuracy in traditional
classifiers might be due to utilizing the images directly for training by
flattening the images into a features matrix. There might be an improvement if
we could have tried with power spectral features but didn’t anticipate
performance comparable with DL models. This cannot preserve the spatial
position as the topo plot does. However, when compared our model with DL
models showed comparable performance in all windows.

###  4.2 Parameters and Time

Parameters define the complexity of a model, we tried to keep our model simple
and explainable so that each block can be understood easily. We compared the
parameters and time for training and testing. Brain2Depth demonstrated
comparable performance while using parameters which were only 0.52% of VGG16,
0.32% of ResNet50, 2.28% of MobileNet, and 3.16% of MobileNetV2. The training
and testing time of our model for 9909 samples and 813 samples were 84.882 and
0.152 seconds whereas the mobilenet took the minimum time in all four models,
which were 214.989 and 0.44 seconds. Fast training may help to develop a model
fast with millions of images and make a quick deployment. Minimum test time is
most important in real-time prediction.

Table 2: The number of trainable parameters in each network with train and test time, respectively. Model | Parameters | Training Time(s) | Testing time(s) |  | Testing per
---
sample(ms)
VGG16 | 14780739 | 652 | 0.473 | 0.582
ResNet50 | 23850371 | 847.483 | 1.119 | 1.376
MobileNet | 3360451 | 214.989 | 0.44 | 0.541
MobileNetV2 | 2422339 | 258.503 | 0.702 | 0.863
Ours | 76627 | 84.882 | 0.152 | 0.187
Table 3: Test performance of each iteration on theta1 band of 2-sec window:
Training performed on 33 subjects and testing on 3 subjects, including all
conditions. Bold values represents two best performance.

Model | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12
---|---|---|---|---|---|---|---|---|---|---|---|---
VGG16 | 0.839 | 0.915 | 0.957 | 0.985 | 0.996 | 1 | 0.998 | 0.988 | 0.998 | 0.999 | 0.997 | 1
RESNET50 | 0.701 | 0.617 | 0.834 | 0.934 | 0.975 | 0.989 | 0.975 | 0.939 | .989 | .998 | .988 | 0.99
Mobilenet | 0.619 | 0.689 | 0.786 | 0.935 | 0.974 | 0.996 | 0.981 | 0.946 | 0.986 | 0.993 | 0.998 | 0.988
MobilenetV2 | 0.626 | 0.674 | 0.7 | 0.864 | 0.935 | 0.975 | 0.985 | 0.931 | 0.985 | 0.996 | 0.994 | 0.982
Ours | 0.797 | 0.852 | 0.865 | 0.946 | 0.987 | 0.997 | 0.998 | 0.915 | 1 | 0.999 | 0.996 | 0.998

###  4.3 Training vs Performance Tradeoff

ResNet, MobileNet, and MobileNetV2 performed a little low when compared with
VGG and Brain2Depth for a 2-sec window in theta1 . We further explored the
subjectwise performance to understand the differences. We found that accuracy
dropped significantly for one and two iterations as shown in the Table 3. In
the next step, we investigated training and validation loss to verify whether
this happened because of overfitting or underfitting. Fig. 5 shows that
training loss is high for three comparatively with others too. This shows that
it may require more training rounds for this specific case however our model
demonstrates that with moderate training it may perform well. And ResNet is a
heavy architecture it may also require a large number of training samples.
Hence with optimal time, our network can be trained efficiently even though
the training sample can be noisy or varying samples.

Figure 5: Training and validation loss of the first iteration in theta1 band
of 2-sec window with respect to all networks. Figure 6: Visualization of
Block1 Layers: Intermediate representation of standard and depthwise
convolution layers in theta2 (7-8 Hz) and alpha1 (9-10 Hz) bands for expert
meditators.

###  4.4 Visualization

We have visualized the intermediate CNN outputs in block1 for two frequency
bands i.e. theta2 and alpha1. Fig. 6 shows the outputs of five filters for 2D
convolution and depthwise convolution in the expert condition. Layers have
learned the different features, more specifically in theta2, the frontal
region has shown the heightened contribution as compared with alpha1. This has
also been studied in meditation research on the role of the frontal midline
region in the experienced meditators [5].

Table 4: Ablation Study: Performance evaluation on 2-sec window with (A)
several changes in the number of filters and kernel sizes from block1 to
block3. (B) change the order of Relu, Batch Normalization, and Max pooling (C)
modify layer with another layer.

###  4.5 Ablation and Performance Studies

We provide detailed ablation and performance studies. We exploited our model
in the following three ways and reported the significant observations.

  1. 1.

_Variation in Filters:_ Different level of granularity in the learning
representation depends on the size and number of filters. Small-size filters
generate fine-grained information, whereas large-size filters represent
coarse-grained information. We varied the number of filters and their sizes
into three blocks as shown in Table 4 A. We kept the two blocks intact and
changed the left block and observed the performance. No. of parameters ranged
from 49K to 91K, and observed performance changes with varying parameters.
Even with 53K parameters in block1, we found a significant performance above
90% in all the bands. This shows that our presented model can be further
customized according to the need.

  2. 2.

_Swapping of components:_ We swapped the positions of batch normalization,
Relu, and max-pooling. We didn’t observe any significant differences as shown
in Table 4 B.

  3. 3.

_Change in layers:_ In each block, we replaced one layer type with another
layer as shown in Table 4 C. We replaced depthwise convolution with standard
convolution. We observed a slight improvement by (1-2)% in all the frequency
bands however parameters got double and increased the training timing by 7
seconds. Hence, this suggests that the model can be efficiently fine-tuned
according to the availability of the resources and other constraints such as
time.

##  5 Conclusion

This study exhibits a pipeline for the EEG classification task, incorporating
steps to create topo images from EEG signals and a lightweight CNN model. In
several medical domains, heavy deep architectures are not required, and a
simple model is needed to produce similar results with less time. Our proposed
study shows state-of-the-art performance while using only 0.52% and 3.16%
parameters of the VGG and MobileNetV2 network, leading to a significant
reduction in train and test time. Our model can be efficiently deployed in
several real time protocols and effectively suited for resource constraint
environment.

##  6 Acknowledgement

We thank SERB and PlayPower Labs for supporting PMRF Fellowship. We thank
FICCI to facilitate this PMRF Fellowship. We thank Kushpal, Pragati Gupta and
Nashra Ahmad for their valuable feedback.

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