ArticlePeerJ. Computer science2021
OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Temporal convolutional transformer for EEG based motor imagery decoding.Scientific reports · 2025Article
- BCINetV1: Integrating Temporal and Spectral Focus Through a Novel Convolutional Attention Architecture for MI EEG Decoding.Sensors (Basel, Switzerland) · 2025Article
- Advancing BCI with a transformer-based model for motor imagery classification.Scientific reports · 2025Article
- Article
- Bidirectional feature pyramid attention-based temporal convolutional network model for motor imagery electroencephalogram classification.Frontiers in neurorobotics · 2024Article
- Deep temporal networks for EEG-based motor imagery recognition.Scientific reports · 2023Article
- An improved model using convolutional sliding window-attention network for motor imagery EEG classification.Frontiers in neuroscience · 2023Article
- [Multi-task motor imagery electroencephalogram classification based on adaptive time-frequency common spatial pattern combined with convolutional neural network].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Subject-Dependent Artifact Removal for Enhancing Motor Imagery Classifier Performance under Poor Skills.Sensors (Basel, Switzerland) · 2022Article
- DeepFeature: feature selection in nonimage data using convolutional neural network.Briefings in bioinformatics · 2021Article
- Discriminating three motor imagery states of the same joint for brain-computer interface.PeerJ · 2021Article
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Authors and funding
3 authors.
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Abstract
A human-computer interaction (HCI) system can be used to detect different categories of the brain wave signals that can be beneficial for neurorehabilitation, seizure detection and sleep stage classification. Research on developing HCI systems using brain wave signals has progressed a lot over the years. However, real-time implementation, computational complexity and accuracy are still a concern. In this work, we address the problem of selecting the appropriate filtering frequency band while also achieving a good system performance by proposing a frequency-based approach using long short-term memory network (LSTM) for recognizing different brain wave signals. Adaptive filtering using genetic algorithm is incorporated for a hybrid system utilizing common spatial pattern and LSTM network. The proposed method (OPTICAL+) achieved an overall average classification error rate of 30.41% and a kappa coefficient value of 0.398, outperforming the state-of-the-art methods. The proposed OPTICAL+ predictor can be used to develop improved HCI systems that will aid in neurorehabilitation and may also be beneficial for sleep stage classification and seizure detection.
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