ArticleSensors (Basel, Switzerland)2020
Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 105 citations in OpenAlex.
- Artificial intelligence for brain-to-speech decoding in paralysis: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Systematic Review of EEG-Based Imagined Speech Classification Methods.Sensors (Basel, Switzerland) · 2024Pooled it
- Mimed speech as an intermediary state between overt and imagined speech production in an electrocorticography study.Scientific reports · 2025Article
- EEG-Based Prediction of Stress Responses to Naturalistic Decision-Making Stimuli in Police Cadets.Sensors (Basel, Switzerland) · 2025Article
- Neural signals, machine learning, and the future of inner speech recognition.Frontiers in human neuroscience · 2025Review
- Exploring Convolutional Neural Network Architectures for EEG Feature Extraction.Sensors (Basel, Switzerland) · 2024Article
- Decoding imagined speech with delay differential analysis.Frontiers in human neuroscience · 2024Article
- Real-Time Navigation in Google Street ViewSensors (Basel, Switzerland) · 2023Article
- Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network.Frontiers in human neuroscience · 2023Article
- Linguistic representation of vowels in speech imagery EEG.Frontiers in human neuroscience · 2023Article
- The Role of Artificial Intelligence in Decoding Speech from EEG Signals: A Scoping Review.Sensors (Basel, Switzerland) · 2022Article
- [Key technology of brain-computer interaction based on speech imagery].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Rethinking the Methods and Algorithms for Inner Speech Decoding and Making Them Reproducible.NeuroSci · 2022Article
- A State-of-the-Art Review of EEG-Based Imagined Speech Decoding.Frontiers in human neuroscience · 2022Review
- Brain Computer Interfaces and Communication Disabilities: Ethical, Legal, and Social Aspects of Decoding Speech From the Brain.Frontiers in human neuroscience · 2022Article
- Silent EEG-Speech Recognition Using Convolutional and Recurrent Neural Network with 85% Accuracy of 9 Words Classification.Sensors (Basel, Switzerland) · 2021Article
- Recognition of EEG Signals from Imagined Vowels Using Deep Learning Methods.Sensors (Basel, Switzerland) · 2021Article
- Decoding Covert Speech From EEG-A Comprehensive Review.Frontiers in neuroscience · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain-computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide significant advances over traditional machine learning (ML) approaches for classification of imagined speech. Furthermore, hyperparameter (HP) optimization has been neglected in DL-EEG studies, resulting in the significance of its effects remaining uncertain. In this study, we aim to improve classification of imagined speech EEG by employing DL methods while also statistically evaluating the impact of HP optimization on classifier performance. We trained three distinct convolutional neural networks (CNN) on imagined speech EEG using a nested cross-validation approach to HP optimization. Each of the CNNs evaluated was designed specifically for EEG decoding. An imagined speech EEG dataset consisting of both words and vowels facilitated training on both sets independently. CNN results were compared with three benchmark ML methods: Support Vector Machine, Random Forest and regularized Linear Discriminant Analysis. Intra- and inter-subject methods of HP optimization were tested and the effects of HPs statistically analyzed. Accuracies obtained by the CNNs were significantly greater than the benchmark methods when trained on both datasets (words: 24.97%,
Indexed as
Identifiers
What OpenQuestion holds
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.