ArticleCognitive neurodynamics2023
The LightGBM-based classification algorithm for Chinese characters speech imagery BCI system.
Article in Cognitive neurodynamics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 33 citations in OpenAlex.
- The current status of foundation models in decoding inner speech from non-invasive brain signals: a mini review.Frontiers in human neuroscience · 2026Review
- A novel stroke classification model based on EEG feature fusion.Scientific reports · 2025Article
- Systematic review: progress in EEG-based speech imagery brain-computer interface decoding and encoding research.PeerJ. Computer science · 2025Article
- Neural decoding and feature selection methods for closed-loop control of avoidance behavior.Journal of neural engineering · 2024Article
- The design and implementation of multi-character classification scheme based on EEG signals of visual imagery.Cognitive neurodynamics · 2024Article
- Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.Scientific reports · 2024Article
- Resting state EEG assisted imagined vowel phonemes recognition by native and non-native speakers using brain connectivity measures.Physical and engineering sciences in medicine · 2024Article
- Adaptive LDA Classifier Enhances Real-Time Control of an EEG Brain-Computer Interface for Decoding Imagined Syllables.Brain sciences · 2024Article
- A Comparative Study of Automated Machine Learning Platforms for Exercise Anthropometry-Based Typology Analysis: Performance Evaluation of AWS SageMaker, GCP VertexAI, and MS Azure.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
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Abstract
Brain-computer interface (BCI) can obtain text information by decoding language induced electroencephalogram (EEG) signals, so as to restore communication ability for patients with language impairment. At present, the BCI system based on speech imagery of Chinese characters has the problem of low accuracy of features classification. In this paper, the light gradient boosting machine (LightGBM) is adopted to recognize Chinese characters and solve the above problems. Firstly, the Db4 wavelet basis function is selected to decompose the EEG signals in six-layer of full frequency band, and the correlation features of Chinese characters speech imagery with high time resolution and high frequency resolution are extracted. Secondly, the two core algorithms of LightGBM, gradient-based one-side sampling and exclusive feature bundling, are used to classify the extracted features. Finally, we verify that classification performance of LightGBM is more accurate and applicable than the traditional classifiers according to the statistical analysis methods. We evaluate the proposed method through contrast experiment. The experimental results show that the average classification accuracy of the subjects' silent reading of Chinese characters "(left)", "(one)" and simultaneous silent reading is improved by 5.24%, 4.90% and 12.44% respectively.
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