Balancing Interpretability and Performance in Motor Imagery EEG Classification: A Comparative Study of ANFIS-FBCSP-PSO and EEGNet

• URL: http://arxiv.org/abs/2511.00369v1
• Date: Sat, 01 Nov 2025 02:42:01 GMT
• Title: Balancing Interpretability and Performance in Motor Imagery EEG Classification: A Comparative Study of ANFIS-FBCSP-PSO and EEGNet
• Authors: Farjana Aktar, Mohd Ruhul Ameen, Akif Islam, Md Ekramul Hamid,
• Abstract summary: This paper compares a transparent fuzzy reasoning approach (ANFIS-FBCSP-PSO) with a deep learning benchmark (EEGNet) using the BCI Competition IV-2a dataset.<n>In within-subject experiments, the fuzzy neural model performed better (68.58 percent +/- 13.76 percent accuracy, kappa = 58.04 percent +/- 18.43)<n>The study provides practical guidance for selecting MI-BCI systems according to design goals: interpretability or robustness across users.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Achieving both accurate and interpretable classification of motor imagery EEG remains a key challenge in brain computer interface (BCI) research. This paper compares a transparent fuzzy reasoning approach (ANFIS-FBCSP-PSO) with a deep learning benchmark (EEGNet) using the BCI Competition IV-2a dataset. The ANFIS pipeline combines filter bank common spatial pattern feature extraction with fuzzy IF-THEN rules optimized via particle swarm optimization, while EEGNet learns hierarchical spatial temporal representations directly from raw EEG data. In within-subject experiments, the fuzzy neural model performed better (68.58 percent +/- 13.76 percent accuracy, kappa = 58.04 percent +/- 18.43), while in cross-subject (LOSO) tests, the deep model exhibited stronger generalization (68.20 percent +/- 12.13 percent accuracy, kappa = 57.33 percent +/- 16.22). The study provides practical guidance for selecting MI-BCI systems according to design goals: interpretability or robustness across users. Future investigations into transformer based and hybrid neuro symbolic frameworks are expected to advance transparent EEG decoding.

Related papers

• CORGI: GNNs with Convolutional Residual Global Interactions for Lagrangian Simulation [32.83908739007555]
  We introduce Convolutional Residual Global Interactions (CORGI), a hybrid architecture that augments any GNN-based solver with a lightweight Eulerian component for global context aggregation.<n>When applied to a GNS backbone, CORGI achieves a 57% improvement in rollout accuracy with only 13% more inference time and 31% more training time.
  arXiv  Detail & Related papers  (2025-11-28T07:26:35Z)
• rECGnition_v2.0: Self-Attentive Canonical Fusion of ECG and Patient Data using deep learning for effective Cardiac Diagnostics [0.56337958460022]
  This study uses MIT-BIH Arrhythmia dataset to evaluate the efficiency of rECGnition_v2.0 for various classes of arrhythmias.<n>The compact architectural footprint of the rECGnition_v2.0, characterized by its lesser trainable parameters, unfurled several advantages including interpretability and scalability.
  arXiv  Detail & Related papers  (2025-02-22T15:16:46Z)
• Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest [35.858837946090674]
  This study introduces CRF-GAN, a novel memory-efficient GAN architecture.<n>The model's performance is evaluated against the state-of-the-art hierarchical (HA)-GAN model.
  arXiv  Detail & Related papers  (2025-01-26T15:57:44Z)
• An AI-Driven Live Systematic Reviews in the Brain-Heart Interconnectome: Minimizing Research Waste and Advancing Evidence Synthesis [29.81784450632149]
  We develop an AI-driven system to enhance systematic reviews in the Brain-Heart Interconnectome (BHI) domain.<n>The system integrates automated detection of Population, Intervention, Comparator, Outcome, and Study design (PICOS), semantic search using vector embeddings, graph-based querying, and topic modeling.<n>The system provides real-time updates, reducing research waste through a living database and offering an interactive interface with dashboards and conversational AI.
  arXiv  Detail & Related papers  (2025-01-25T03:51:07Z)
• EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks [7.9035081192335115]
  We present the EEG_RL-Net, an enhancement to the EEG_GLT-Net framework, which incorporates the trained EEG GCN Block from EEG_GLT-Net at an adjacency matrix density of 13.39%. The EEG_RL-Net model showcases exceptional classification performance, achieving an unprecedented average accuracy of 96.40% across 20 subjects within 25 milliseconds.
  arXiv  Detail & Related papers  (2024-04-26T13:09:50Z)
• Improved Motor Imagery Classification Using Adaptive Spatial Filters Based on Particle Swarm Optimization Algorithm [4.93693103484175]
  This paper proposes an adaptive spatial filter solving method based on particle swarm optimization algorithm (PSO) A training and testing framework based on filter bank and spatial filters (FBCSP-ASP) is designed for MI EEG signal classification. The classification accuracy of the proposed method has reached 74.61% and 81.19% on datasets 2a and 2b, respectively.
  arXiv  Detail & Related papers  (2023-10-29T23:53:37Z)
• EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification [79.7946379395238]
  We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
  arXiv  Detail & Related papers  (2023-10-24T02:37:49Z)
• Fuzzy temporal convolutional neural networks in P300-based Brain-computer interface for smart home interaction [3.726817037277484]
  EEG patterns exhibit high variability across time and uncertainty due to noise. It is a significant problem to be addressed in P300-based Brain Computer Interface for smart home interaction. We propose a sequential unification of temporal convolutional networks (TCNs) modified to EEG signals, LSTM cells, with a fuzzy neural block (FNB)
  arXiv  Detail & Related papers  (2022-04-09T00:35:35Z)
• Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG [56.155331323304]
  Deep learning based electroencephalogram channels' feature level fusion is carried out in this work. Channel selection, fusion, and classification procedures were optimized by two optimization algorithms.
  arXiv  Detail & Related papers  (2021-12-18T14:17:49Z)
• CNN-based Approaches For Cross-Subject Classification in Motor Imagery: From The State-of-The-Art to DynamicNet [0.2936007114555107]
  Motor imagery (MI)-based brain-computer interface (BCI) systems are being increasingly employed to provide alternative means of communication and control. accurately classifying MI from brain signals is essential to obtain reliable BCI systems. Deep learning approaches have started to emerge as valid alternatives to standard machine learning techniques.
  arXiv  Detail & Related papers  (2021-05-17T14:57:13Z)
• EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery Classification [123.93460670568554]
  This paper proposes a novel convolutional neural network (CNN) architecture for accurate and robust EEG-based motor imagery (MI) classification. The proposed CNN model, namely EEG-Inception, is built on the backbone of the Inception-Time network. The proposed network is an end-to-end classification, as it takes the raw EEG signals as the input and does not require complex EEG signal-preprocessing.
  arXiv  Detail & Related papers  (2021-01-24T19:03:10Z)
• A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery [164.93739293097605]
  Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI) This study modifies the conventional CSP algorithm to improve the multi-class MI classification accuracy and ensure the computation process is efficient.
  arXiv  Detail & Related papers  (2020-08-25T18:23:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.