# ScalingNet: extracting features from raw EEG data for emotion recognition

arXiv: https://arxiv.org/abs/2105.13987 · HTML: https://mlsanity.com/paper/2105.13987/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Identifying Stable Patterns over Time for Emotion Recognition from EEG](https://mlsanity.com/paper/1601.02197/) (2016)
- [Multimodal Emotion Recognition Using Multimodal Deep Learning](https://mlsanity.com/paper/1602.08225/) (2016)

## Built on by (most cited first, 4 of 4)

- [GANSER: A Self-supervised Data Augmentation Framework for EEG-based Emotion Recognition](https://mlsanity.com/paper/2109.03124/) (2021)
- [Neurocognitive Modeling for Text Generation: Deep Learning Architecture for EEG Data](https://mlsanity.com/paper/2509.07202/) (2025)
- [The Role of Review Process Failures in Affective State Estimation: An Empirical Investigation of DEAP Dataset](https://mlsanity.com/paper/2508.02417/) (2025)
- [Atoms of Thought: Universal EEG Representation Learning with Microstates](https://mlsanity.com/paper/2605.20182/) (2026)

## Abstract (arXiv)

Convolutional Neural Networks(CNNs) has achieved remarkable performance breakthrough in a variety of tasks. Recently, CNNs based methods that are fed with hand-extracted EEG features gradually produce a powerful performance on the EEG data based emotion recognition task. In this paper, we propose a novel convolutional layer allowing to adaptively extract effective data-driven spectrogram-like features from raw EEG signals, which we reference as scaling layer. Further, it leverages convolutional kernels scaled from one data-driven pattern to exposed a frequency-like dimension to address the shortcomings of prior methods requiring hand-extracted features or their approximations. The proposed neural network architecture based on the scaling layer, references as ScalingNet, has achieved the state-of-the-art result across the established DEAP benchmark dataset.