Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Electrooculogram (EOG) contamination is a common critical issue in general EEG studies as ll as in building highperformance brain computer interfaces (BCI). Existing regression or independent component analysis based artefacts correction methods are usually not applicable when EOG is not available or when there are very few EEG channels. ,  propose a novel ocular artefacts correction method for processing EEG without using dedicated EOG channels. The method constructs estimate of ocular components through artefacts detection in EEG. Then, an optimization based on logistic regression is introduced to remove the components from EEG. Specifically, the optimization ensures that the discriminative information is maintained in the corrected EEG signals. The proposed method is offline evaluated with a large EEG data set containing 68 subjects. Experimental results show that, through the artefacts removal correction by the proposed method, EEG classification accuracy can be improved with statistical significance.
['Xinyang Li', 'Cuntai Guan', 'Kai Keng Aug', 'Chuanchu Wang', 'Zheng Yang Chin', 'Haihong Zhang', 'Choon Guan Lim', 'Tih Shih Lee']
I am writing a research paper on a idea An ocular artefacts correction method for discriminative EEG analysis based on logistic regression
[{"from": "human", "value": "I am writing a research paper on a idea An ocular artefacts correction method for discriminative EEG analysis based on logistic regression"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Electrooculogram (EOG) contamination is a common critical issue in general EEG studies as ll as in building highperformance brain computer interfaces (BCI). Existing regression or independent component analysis based artefacts correction methods are usually not applicable when EOG is not available or when there are very few EEG channels. ,  propose a novel ocular artefacts correction method for processing EEG without using dedicated EOG channels. The method constructs estimate of ocular components through artefacts detection in EEG. Then, an optimization based on logistic regression is introduced to remove the components from EEG. Specifically, the optimization ensures that the discriminative information is maintained in the corrected EEG signals. The proposed method is offline evaluated with a large EEG data set containing 68 subjects. Experimental results show that, through the artefacts removal correction by the proposed method, EEG classification accuracy can be improved with statistical significance."}]