# Motor Imagery EEG Signal Classification Using Minimally Random Convolutional Kernel Transform and Hybrid Deep Learning

arXiv: https://arxiv.org/abs/2508.16179 · HTML: https://mlsanity.com/paper/2508.16179/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 106 references that are in the map.
- First posted on arXiv 22 Aug 2025.

## Builds on (most cited first, 9 of 9)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [Linked Component Analysis from Matrices to High Order Tensors: Applications to Biomedical Data](https://mlsanity.com/paper/1508.07416/) (2015)
- [GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals](https://mlsanity.com/paper/2006.08924/) (2020)
- [EEG-ITNet: An Explainable Inception Temporal Convolutional Network for Motor Imagery Classification](https://mlsanity.com/paper/2204.06947/) (2022)
- [Intent Recognition in Smart Living Through Deep Recurrent Neural Networks](https://mlsanity.com/paper/1702.06830/) (2017)
- [Drug-Drug Interaction Extraction from Biomedical Text Using Long Short Term Memory Network](https://mlsanity.com/paper/1701.08303/) (2017)
- [Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition](https://mlsanity.com/paper/2005.00777/) (2020)

## Abstract (arXiv)

The brain-computer interface (BCI) establishes a non-muscle channel that enables direct communication between the human body and an external device. Electroencephalography (EEG) is a popular non-invasive technique for recording brain signals. It is critical to process and comprehend the hidden patterns linked to a specific cognitive or motor task, for instance, measured through the motor imagery brain-computer interface (MI-BCI). A significant challenge is presented by classifying motor imagery-based electroencephalogram (MI-EEG) tasks, given that EEG signals exhibit nonstationarity, time-variance, and individual diversity. Obtaining good classification accuracy is also very difficult due to the growing number of classes and the natural variability among individuals. To overcome these issues, this paper proposes a novel method for classifying EEG motor imagery signals that extracts features efficiently with Minimally Random Convolutional Kernel Transform (MiniRocket), a linear classifier then uses the extracted features for activity recognition. Furthermore, a novel deep learning based on Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) architecture to serve as a baseline was proposed and demonstrated that classification via MiniRocket's features achieves higher performance than the best deep learning models at lower computational cost. The PhysioNet dataset was used to evaluate the performance of the proposed approaches. The proposed models achieved mean accuracy values of 98.63% and 98.06% for the MiniRocket and CNN-LSTM, respectively. The findings demonstrate that the proposed approach can significantly enhance motor imagery EEG accuracy and provide new insights into the feature extraction and classification of MI-EEG.