ArticleProceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference)2019
Classification of Brainwaves Using Convolutional Neural Network.
Article in Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Federated Learning Framework for Brain Tumor Detection Using MRI Images in Non-IID Data Distributions.Journal of imaging informatics in medicine · 2025Article
- An Integrative Review of Brainwave Entrainment Benefits for Human Health.Applied psychophysiology and biofeedback · 2025Review
- Efficacy of Dynamics-based Features for Machine Learning Classification of Renal Hemodynamics.Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference) · 2023Article
- Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images.Diagnostics (Basel, Switzerland) · 2023Article
- Parallelistic Convolution Neural Network Approach for Brain Tumor Diagnosis.Diagnostics (Basel, Switzerland) · 2022Article
- Autoregulatory Efficiency Assessment in Kidneys Using Deep Learning.Proceedings of the ... European Signal Processing Conference (EUSIPCO). EUSIPCO (Conference) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Classification of brainwaves in recordings is of considerable interest to neuroscience and medical communities. Classification techniques used presently depend on the extraction of low-level features from the recordings, which in turn affects the classification performance. To alleviate this problem, this paper proposes an end-to-end approach using Convolutional Neural Network (CNN) which has been shown to detect complex patterns in a signal by exploiting its spatiotemporal nature. The present study uses time and frequency axes for the classification using synthesized Local Field Potential (LFP) data. The results are analyzed and compared with the FFT technique. In all the results, the CNN outperforms the FFT by a significant margin especially when the noise level is high. This study also sheds light on certain signal characteristics affecting network performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.