# Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral
Physiological Signals

###### Abstract

Multimodal emotion recognition from physiological signals is receiving an
increasing amount of attention due to the impossibility to control them at
will unlike behavioral reactions, thus providing more reliable information.
Existing deep learning-based methods still rely on extracted handcrafted
features, not taking full advantage of the learning ability of neural
networks, and often adopt a single-modality approach, while human emotions are
inherently expressed in a multimodal way. In this paper, we propose a
hypercomplex multimodal network equipped with a novel fusion module comprising
parameterized hypercomplex multiplications. Indeed, by operating in a
hypercomplex domain the operations follow algebraic rules which allow to model
latent relations among learned feature dimensions for a more effective fusion
step. We perform classification of valence and arousal from
electroencephalogram (EEG) and peripheral physiological signals, employing the
publicly available database MAHNOB-HCI surpassing a multimodal state-of-the-
art network. The code of our work is freely available at
https://github.com/ispamm/MHyEEG.

Index Terms— Hypercomplex Neural Networks, Hypercomplex Algebra, EEG,
Multimodal Emotion Recognition

##  1 Introduction

Emotion is an essential part of human communication that plays a vital role in
the overall quality and outcome of interactions. Thus, automatic emotion
recognition and affective computing have gained much interest, also
considering the wide range of applications in human-computer interaction (HCI)
[1]. Humans manifest emotions in a multimodal way, including facial
expressions, speech, body language, and physiological signals. While
behavioral reactions can be easily controlled, for example, real emotion can
be concealed by adjusting expressions or tone of voice, physiological signals
cannot be governed at will, thus being more reliable for recognizing human
emotion [2]. Therefore, on account of the development of non-invasive and
inexpensive wearable devices, physiological-based emotion recognition has
become a hot topic in affective computing research. Among these,
electroencephalography (EEG) is a measure of the electrical activity of the
brain that is directly correlated with the cognitive process and can provide
key information regarding emotional states being characterized by excellent
temporal resolution [3]. Therefore, EEG-based analysis has received an
increasing amount of attention for a variety of applications such as epileptic
seizure detection [4], general EEG classification [5] and emotion recognition
[6].

Nevertheless, most studies do not take full advantage of the learning ability
of deep learning models and most of the time focus on a single-modality
approach. In fact, the input to the neural model is often extracted features
instead of the raw data and corresponding to a single modality, generally EEG,
when in reality human emotions are intrinsically multimodal, with different
modalities describing different aspects of an emotional reaction and
correlations among them providing critical information if exploited correctly
[2]. Recent works have started to take a multimodal approach, but most rely on
trivial techniques and few studies explore more emerging paradigms such as
multimodal learning [7]. Therefore, effectively learning from multiple
physiological signals to produce more powerful feature representations is
still an open problem. Motivated by the described challenges, in this paper we
address the more difficult approach of learning directly from raw signals and
propose a multimodal architecture with a novel fusion module that exploits the
properties of algebras in the hypercomplex domain to truly take advantage of
correlations characteristic of EEG and peripheral physiological signals.

Parameterized hypercomplex neural networks (PHNNs) are an emerging family of
models which operate in a hypercomplex number domain [8, 9]. They have been
introduced in order to generalize the more common quaternion neural networks
(QNNs) which are defined in the quaternion domain and are thus limited to $4$D
input data but possess very powerful capabilities [10]. In fact, thanks to
quaternion algebra operations, such as the Hamilton product, these models are
endowed with the ability to capture not only global relations as any neural
network but also local relations among input data, unlike real-valued
counterparts, as well as being more lightweight [11]. Thanks to the
introduction of parameterized hypercomplex multiplication (PHM) and
convolution (PHC), these advantages have been extended to inputs of any
dimensionality $n$, with a reduction of parameters of $1/n$.

Owing to these advantages, we design a hypercomplex multimodal network with a
novel fusion module defined in the hypercomplex domain, thus comprising PHM
layers that thanks to hypercomplex algebra properties endow the architecture
with the ability to model correlations among the learned latent features, thus
learning a more effective fused representation. Specifically, we perform
classification of valence and arousal from EEG, electrocardiogram (ECG),
galvanic skin response (GSR), and eye data, and we validate the proposed
approach on a publicly available benchmark, MAHNOB-HCI [12], showing how our
method outperforms a multimodal state-of-the-art network.

##  2 Background

A plethora of machine learning approaches for emotion recognition have been
proposed [13, 14, 15]. However, employing such methods requires extensive
domain knowledge to extract relevant features. On the other hand, deep
learning models are able to learn features directly from the raw data and thus
learn a powerful latent representation. Due to these advantages, many deep
learning-based methods have been investigated [16, 17, 18, 19]. Nonetheless,
even though such works employ neural networks, they still rely on extracted
features, such as power spectral density (PSD) and differential entropy (DE),
instead of taking full advantage of the representational learning ability of
neural models. Rather, a study that employs raw EEG signals has proposed a
$3$D representation of the data to be processed by a $3$D convolutional neural
network (CNN) [20]. However, all aforementioned methods focus on a single-
modality approach which is suboptimal [2]. Thus in order to exploit the
information contained in different modalities, recent studies adopt a
multimodal approach for emotion recognition, some still relying on extracted
features [2, 21, 22] and very few that directly employ raw data [23, 24, 25],
where the latter focuses on perceived mental workload classification instead
of emotion recognition.

Aside from feature extraction, the crucial step of multimodal learning is the
fusion strategy. Surely, much of the research in this field has focused on
this aspect, as there are a multitude of manners to incorporate information
from different modalities. Starting from the most trivial, i.e. early fusion
in which data from different modalities is concatenated to form a single input
to the model, and late fusion which consists in aggregating decisions of
different networks trained separately on each modality to obtain a final
output. Both strategies suffer from several problems, where the first does not
take into account the different nature of the input modalities, not taking
advantage of complementary information and not allowing to identify relations
among them, while the second does not exploit cross-modal information during
learning at all, also requiring to optimize a different network for each
modality. Instead, more complex strategies fall under the name of intermediate
fusion, which consists in first learning modality-specific latent
representations that are subsequently fused together for further processing
[7]. Thus, in this paper, we investigate and propose a novel technique that
allows to effectively grasp correlations between the different modalities
during learning thanks to its definition in a hypercomplex algebraic system.

##  3 Methodology

Fig. 1: HyperFuseNet architecture. The encoder learns modality-specific latent
representations in the real domain, which are then merged together and
processed by the novel fusion module in the hypercomplex domain with $n=4$.
Finally, a fully-connected layer produces the prediction for arousal/valence.

###  3.1 Hypercomplex neural models

Hypercomplex neural networks are neural models defined in a hypercomplex
number system ${\mathbb{H}}$ which is regulated by the respective algebra
rules that define addition and multiplication operations. A generic
hypercomplex number is defined as

$h=h_{0}+h_{i}{\hat{\imath}}_{i}+\ldots+h_{n}{\hat{\imath}}_{n},\qquad
i=1,\ldots,n$ (1)

where $h_{0},\ldots,h_{n}\in\mathbb{R}$ and
${\hat{\imath}}_{i},\ldots,{\hat{\imath}}_{n}\in{\mathbb{H}}$ are the
imaginary units.

The general hypercomplex domain ${\mathbb{H}}$ includes various algebraic
systems such as the complex ${\mathbb{C}}$ domain when $n=2$ and the
quaternion ${\mathbb{Q}}$ domain when $n=4$, where quaternion neural networks
(QNNs) operate in. In fact, algebra rules are defined only at predefined
dimensions of $n=2^{m}$, with $m\in\mathbb{N}$, owing to the fact that
hypercomplex algebras are included in the family of Cayley-Dickson algebras.
Thus, each of these number systems is identified by the number of imaginary
units and consequently by the different definitions of the multiplication
operation as a result of the disparate interactions among imaginary units. For
example, in the quaternion domain, the product is non-commutative, with
${\hat{\imath}}_{1}{\hat{\imath}}_{2}\neq{\hat{\imath}}_{2}{\hat{\imath}}_{1}$.
Therefore, in the latter domain, the Hamilton product was introduced, which
also regulates the matrix multiplication in fully connected layers and the
convolution operation in convolutional layers, since both the weight matrix
and the input are encapsulated into a quaternion in the following way:
${\mathbf{W}}={\mathbf{W}}_{0}+{\mathbf{W}}_{1}{\hat{\imath}}_{1}+{\mathbf{W}}_{2}{\hat{\imath}}_{2}+{\mathbf{W}}_{3}{\hat{\imath}}_{3}$
and
${\mathbf{x}}={\mathbf{x}}_{0}+{\mathbf{x}}_{1}{\hat{\imath}}_{1}+{\mathbf{x}}_{2}{\hat{\imath}}_{2}+{\mathbf{x}}_{3}{\hat{\imath}}_{3}$,
respectively. As a consequence, the matrix multiplication of a general fully
connected layer becomes

${\bf{W}}{\bf{x}}=\left[{\begin{array}[]{*{20}c}\hfill{{\bf{W}}_{0}}&\hfill{-{\bf{W}}_{1}}&\hfill{-{\bf{W}}_{2}}&\hfill{-{\bf{W}}_{3}}\\\
\hfill{{\bf{W}}_{1}}&\hfill{{\bf{W}}_{0}}&\hfill{-{\bf{W}}_{3}}&\hfill{{\bf{W}}_{2}}\\\
\hfill{{\bf{W}}_{2}}&\hfill{{\bf{W}}_{3}}&\hfill{{\bf{W}}_{0}}&\hfill{-{\bf{W}}_{1}}\\\
\hfill{{\bf{W}}_{3}}&\hfill{-{\bf{W}}_{2}}&\hfill{{\bf{W}}_{1}}&\hfill{{\bf{W}}_{0}}\\\
\end{array}}\right]\left[{\begin{array}[]{*{20}c}{{\bf{x}}_{0}\hfill}\\\
{{\bf{x}}_{1}\hfill}\\\ {{\bf{x}}_{2}\hfill}\\\ {{\bf{x}}_{3}\hfill}\\\
\end{array}}\right].$ (2)

From eq. (2) it can be seen that the filter submatrices are shared among input
dimensions, thus not only reducing the number of free parameters by $1/4$,
resulting in a more lightweight model, but additionally endowing the neural
network with the ability to grasp latent relations among channel dimensions.
Nonetheless, QNNs are limited to $4$D inputs, therefore parameterized
hypercomplex multiplication (PHM) [8] and convolution (PHC) [9] have been
introduced to bridge this gap. The core idea of these methods lies in
expressing the weight matrix as a sum of $n\in\mathbb{N}$ Kronecker products,
thus we have

${\mathbf{W}}=\sum_{i=0}^{n}\mathbf{A}_{i}\otimes\mathbf{F}_{i},$ (3)

whereby matrices $\mathbf{A}_{i}$ encode the algebra rules, directly learned
from the data, and $\mathbf{F}_{i}$ represent the filters. As a result of eq.
(3), a parameterization of ${\mathbf{W}}$ is obtained, meaning that $n$ is a
user-defined hyperparameter that decides in which domain the neural model
operates (e.g., $n=4$ for the quaternion domain), thus extending the
aforementioned properties of QNNs to general input domains $n$D. Specifically,
PHM and PHC layers employ $1/n$ free parameters with respect to real-valued
counterparts and still possess the ability to model correlations present in
the data, unlike real-valued networks.

###  3.2 Multimodal Hypercomplex Fusion Network

To address the challenges presented in Section 2 we propose HyperFuseNet, a
multimodal architecture that exploits hypercomplex algebra properties to
effectively fuse the learned latent representations as can be seen in Fig. 1.
The neural model comprises two main components, that is the encoder and a
hypercomplex fusion module. Concretely, the encoder is composed of four
different branches in the real domain, one for each modality, and has the
objective of learning modality-specific latent representations directly from
the raw signals, thus with a modality-level focus. Thereafter, these features
in the latent space are merged together and processed by the proposed
hypercomplex fusion module. The latter is composed of PHM layers with the
hyperparameter $n$ set to $4$, as there are four feature vectors in input to
the module corresponding to the four modalities, and has the role of learning
a fused representation, thus performing a patient-level analysis. More in
detail, by defining multiplications in the hypercomplex domain, the proposed
fusion module possesses the capability of grasping cross-modal interactions
between the learned latent features of the EEG, ECG, GSR, and eye data
signals, which are highly correlated. Thus, the hypercomplex fusion module
captures both global and local relations between feature dimensions, unlike
real-valued networks, accordingly learning a more powerful representation by
truly exploiting the correlations present in the different physiological
signals.

##  4 Experimental results

###  4.1 Dataset

To validate the proposed approach we adopt a publicly available dataset, that
is MAHNOB-HCI [12]. It is a multimodal dataset for affect recognition which
includes synchronized recordings of face video, audio signal, eye gaze data,
and peripheral/central nervous system physiological signals of $27$
participants while watching emotional video clips. The eye gaze data comprises
pupil dimensions, gaze coordinates, and eye distances, while for the
physiological signals, we focus on EEG, ECG, and GSR, as these are highly
related to emotional changes [12]. The database provides labels related to
arousal, i.e., calm, medium aroused, and excited, and valence, i.e.,
unpleasant, neutral valence, and pleasant.

###  4.2 Preprocessing and data augmentation

Table 1: Results on MAHNOB-HCI of the proposed method compared against a state-of-the-art model with and without data augmentation. Model | Augm. | Arousal |  | Valence
---|---|---|---|---
F1-score | Accuracy |  | F1-score | Accuracy
Dolmans [25] | ✗ | 36.60 $\pm$ 1.61 |  41.23 $\pm$ 2.03 |  | 37.44 $\pm$ 3.22 | 41.89 $\pm$ 3.34
HyperFuseNet (ours) |  38.83 $\pm$ 1.66 | 40.02 $\pm$ 1.98 |  |  41.43 $\pm$ 1.62 |  43.42 $\pm$ 2.57
Dolmans [25] | ✓ | 38.86 $\pm$ 1.11 | 40.90 $\pm$ 0.62 |  | 38.33 $\pm$ 1.24 | 40.24 $\pm$ 1.04
HyperFuseNet (ours) |  39.65 $\pm$ 1.75 |  41.56 $\pm$ 1.33 |  |  43.60 $\pm$ 2.22 |  44.30 $\pm$ 2.01

Firstly, we downsample EEG, ECG, and GSR signals from $256$Hz to $128$Hz,
while we keep eye data at $60$Hz. Then, we filter EEG and ECG signals with a
band-pass filter at $1$-$45$Hz and $0.5$-$45$Hz, respectively [14, 23], while
a low-pass filter at $60$Hz is applied to GSR signals [26], and for all of
them an additional notch filter at $50$Hz [16], with all EEG signals being
firstly referenced to average. Additionally, we perform a baseline correction
on the GSR signal with respect to the mean value within the $200$ms preceding
each trial to eliminate the initial offset of the signal. Finally, as for EEG
data, we select $10$ channels out of the original $32$, i.e., F$3$, F$4$,
F$7$, F$8$, FC$5$, FC$6$, T$7$, T$8$, P$7$, and P$8$, as these are the most
related to emotion [27, 28]. Instead, regarding eye data, we take the average
between the signals related to the two eyes and we keep $-1$ values as they
correspond to blinks or rapid movements which are relevant to the task at
hand.

We extract samples by dividing the last $30$s of each trial into three
segments of $10$s, as measurements toward the end of the clips reflect the
emotion of the subjects’ rating [23]. Finally, we split the dataset in a
stratified fashion by taking $20\%$ of the data for testing. Training samples
are then augmented by applying scaling and noise addition. Firstly, two
scaling factors are uniformly sampled over two intervals, i.e., $[0.7,0.8]$
and $[1.2,1.3]$, and applied to the original sample to generate two augmented
versions. Then, a Gaussian noise signal with zero mean is added to each
sample, with its standard deviation being computed modality-wise such that the
augmented signal has a signal-to-noise ratio (SNR) of 5dB. A total of $30$
augmented signals are generated for each original sample.

###  4.3 Architecture and training recipe

The proposed architecture comprises four branches that compose the encoder and
a hypercomplex fusion module. The branches consist of three fully-connected
layers, except for the GSR branch which has two, with $128$ units for eye data
and GSR, $512$ for ECG, and $1024$ for EEG, interleaved with batch
normalization and ReLU activation function, inspired by [25]. Then, the
learned latent representations are merged together and processed by the
proposed fusion module which comprises four PHM layers with $n=4$, with the
same interleaved layers and the number of units halved at each layer, a
dropout layer, and the final output layer. The model is trained using the Adam
optimizer, with a categorical cross-entropy loss and a one-cycle policy. The
best hyperparameters are found by doing a bayesian search, sampling the
learning rate from $[0.001,0.008]$. The number of epochs is set to $100$ with
early stopping with patience at $20$.

###  4.4 Results

We report in Tab. 1 the results of the conducted experimental analysis,
showing the mean over $3$ runs of the F1-score and accuracy, which indeed is
not always representative due to imbalance of classes. In detail, we compare
the proposed architecture against a state-of-the-art multimodal network that
also operates with raw signals and is originally designed for mental workload
classification. We train it using the same approach we employed for our
network on the same database. Firstly, we can observe that the employed data
augmentation is effective and improves the performance of both networks.
Secondly, and most importantly, the proposed hypercomplex architecture
outperforms the method employed as comparison in both augmentation scenarios,
thus demonstrating the efficacy of the PHM layers in the fusion step which
yield better emotion recognition accuracy as a result of the grasped cross-
modal correlations thanks to hypercomplex algebra rules.

##  5 Conclusion

In this paper, we proposed a multimodal architecture with a novel hypercomplex
fusion module for emotion recognition from EEG and peripheral physiological
signals, in which a modality-specific representation is firstly learned in the
real domain and consequently processed together by the fusion module in the
hypercomplex domain. The latter was found to be effective to perform a more
proper fusion step than classical real-valued fully-connected layers, in fact,
by employing hypercomplex multiplications the module is capable of capturing
relations among the learned latent features and as a result learn a more
discriminant representation. In future efforts, we aim at additionally
exploiting intra-modality correlations with parameterized hypercomplex
convolutions, thus bringing the advantages of the fusion step also at the
encoder level.

## References

  * [1] M. Wu, S. Hu, B. Wei, and Z. Lv, “A novel deep learning model based on the ICA and Riemannian manifold for EEG-based emotion recognition,” _Journal of Neuroscience Methods_ , vol. 378, p. 109642, 2022.
  * [2] Y. Zhang, C. Cheng, and Y. Zhang, “Multimodal emotion recognition based on manifold learning and convolution neural network,” _Multimedia Tools and Applications_ , vol. 81, no. 23, pp. 33 253–33 268, 2022.
  * [3] Z. Zhang, S.-h. Zhong, and Y. Liu, “GANSER: A self-supervised data augmentation framework for EEG-based emotion recognition,” _IEEE Trans. on Affective Computing_ , pp. 1–1, 2022.
  * [4] P. Boonyakitanont, A. Lek-Uthai, and J. Songsiri, “Automatic epileptic seizure onset-offset detection based on CNN in scalp EEG,” in _IEEE Int. Conf. on Acoust., Speech and Signal Process. (ICASSP)_. IEEE, 2020, pp. 1225–1229.
  * [5] C. Tan, F. Sun, and W. Zhang, “Deep transfer learning for EEG-based brain computer interface,” in _IEEE Int. Conf. on Acoust., Speech and Signal Process. (ICASSP)_ , 2018, pp. 916–920.
  * [6] C. Li, B. Chen, Z. Zhao, N. Cummins, and B. W. Schuller, “Hierarchical attention-based temporal convolutional networks for EEG-based emotion recognition,” in _IEEE Int. Conf. on Acoust., Speech and Signal Process. (ICASSP)_ , 2021, pp. 1240–1244.
  * [7] S. R. Stahlschmidt, B. Ulfenborg, and J. Synnergren, “Multimodal deep learning for biomedical data fusion: a review,” _Briefings in Bioinformatics_ , vol. 23, no. 2, p. bbab569, 2022.
  * [8] A. Zhang, Y. Tay, S. Zhang, A. Chan, A. T. Luu, S. C. Hui, and J. Fu, “Beyond fully-connected layers with quaternions: Parameterization of hypercomplex multiplications with $1/n$ parameters,” _Int. Conf. on Machine Learning (ICML)_ , 2021.
  * [9] E. Grassucci, A. Zhang, and D. Comminiello, “PHNNs: Lightweight neural networks via parameterized hypercomplex convolutions,” _IEEE Trans. on Neural Netwowrks and Learning Systems_ , pp. 1–13, dec 2022.
  * [10] T. Parcollet, M. Morchid, and G. Linarès, “A survey of quaternion neural networks,” _Artif. Intell. Rev._ , Aug. 2019.
  * [11] ——, “Quaternion convolutional neural networks for heterogeneous image processing,” in _IEEE Int. Conf. on Acoust., Speech and Signal Process. (ICASSP)_ , Brighton, UK, May 2019, pp. 8514–8518.
  * [12] M. Soleymani, J. Lichtenauer, T. Pun, and M. Pantic, “A multimodal database for affect recognition and implicit tagging,” _IEEE Trans. on Affective Computing_ , vol. 3, no. 1, pp. 42–55, 2011.
  * [13] Z. He, N. Zhuang, G. Bao, Y. Zeng, and B. Yan, “Cross-day EEG-based emotion recognition using transfer component analysis,” _Electronics_ , vol. 11, no. 4, p. 651, 2022.
  * [14] Y.-J. Liu, M. Yu, G. Zhao, J. Song, Y. Ge, and Y. Shi, “Real-time movie-induced discrete emotion recognition from EEG signals,” _IEEE Trans. on Affective Computing_ , vol. 9, no. 4, pp. 550–562, 2017.
  * [15] L. A. Martínez-Tejada, A. Puertas-González, N. Yoshimura, and Y. Koike, “Exploring EEG characteristics to identify emotional reactions under videogame scenarios,” _Brain Sciences_ , vol. 11, no. 3, p. 378, 2021\.
  * [16] J.-H. Maeng, D.-H. Kang, and D.-H. Kim, “Deep learning method for selecting effective models and feature groups in emotion recognition using an asian multimodal database,” _Electronics_ , vol. 9, no. 12, p. 1988, 2020.
  * [17] X. Du, C. Ma, G. Zhang, J. Li, Y.-K. Lai, G. Zhao, X. Deng, Y.-J. Liu, and H. Wang, “An efficient LSTM network for emotion recognition from multichannel EEG signals,” _IEEE Trans. on Affective Computing_ , vol. 13, no. 3, pp. 1528–1540, 2022.
  * [18] K.-Y. Wang, Y.-L. Ho, Y.-D. Huang, and W.-C. Fang, “Design of intelligent EEG system for human emotion recognition with convolutional neural network,” in _IEEE Int. Conf. on Artificial Intelligence Circuits and Systems (AICAS)_ , 2019, pp. 142–145.
  * [19] S. Rayatdoost and M. Soleymani, “Cross-corpus EEG-based emotion recognition,” in _Int. Workshop on Machine Learning for Signal Processing (MLSP)_ , 2018, pp. 1–6.
  * [20] E. S. Salama, R. A. El-Khoribi, M. E. Shoman, and M. A. W. Shalaby, “EEG-based emotion recognition using 3D convolutional neural networks,” _International Journal of Advanced Computer Science and Applications_ , vol. 9, no. 8, 2018.
  * [21] C. Tan, G. Ceballos, N. Kasabov, and N. Puthanmadam Subramaniyam, “FusionSense: Emotion classification using feature fusion of multimodal data and deep learning in a brain-inspired spiking neural network,” _Sensors_ , vol. 20, no. 18, p. 5328, 2020.
  * [22] S. Rayatdoost, D. Rudrauf, and M. Soleymani, “Expression-guided EEG representation learning for emotion recognition,” in _IEEE Int. Conf. on Acoust., Speech and Signal Process. (ICASSP)_ , 2020, pp. 3222–3226.
  * [23] F. Zeng, Y. Lin, P. Siriaraya, D. Choi, and N. Kuwahara, “Emotion detection using EEG and ECG signals from wearable textile devices for elderly people,” _Journal of Textile Engineering_ , vol. 66, no. 6, pp. 109–117, 2020.
  * [24] B. Nakisa, M. N. Rastgoo, A. Rakotonirainy, F. Maire, and V. Chandran, “Automatic emotion recognition using temporal multimodal deep learning,” _IEEE Access_ , vol. 8, pp. 225 463–225 474, 2020.
  * [25] T. C. Dolmans, M. Poel, J.-W. J. van’t Klooster, and B. P. Veldkamp, “Perceived mental workload classification using intermediate fusion multimodal deep learning,” _Frontiers in human neuroscience_ , vol. 14, p. 609096, 2021.
  * [26] J. A. Miranda-Correa, M. K. Abadi, N. Sebe, and I. Patras, “AMIGOS: A dataset for affect, personality and mood research on individuals and groups,” _IEEE Trans. on Affective Computing_ , vol. 12, no. 2, pp. 479–493, 2018.
  * [27] A. Topic, M. Russo, M. Stella, and M. Saric, “Emotion recognition using a reduced set of EEG channels based on holographic feature maps,” _Sensors_ , vol. 22, no. 9, p. 3248, 2022.
  * [28] J. R. Msonda, Z. He, and C. Lu, “Feature reconstruction based channel selection for emotion recognition using EEG,” in _IEEE Signal Processing in Medicine and Biology Symposium (SPMB)_ , 2021, pp. 1–7.