Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally,  present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s;  also define a method to find a template that best represents, for a given electrode, the subject’s P300 based on his/her own acquired signals. Our experiments with 21 subjects shod that the SWLDA’s performance using our shape-feature vector was , that is,  higher than the one obtained with BCI2000-feature’s vector. The shape-feature vector is 34-dimensional for every electrode; hover, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm shod an averaged area under the ROC (AUROC) curve of . Also, most of the subjects needed less than  trials to have an AUROC superior to . Finally,  found that the electrode C4 also leads to better classification.
['Montserrat Alvarado-González', 'Edgar Garduño', 'Ernesto Bribiesca', 'Oscar Yanez-Suarez', 'Verónica Medina-Bañuelos']
I am writing a research paper on a idea P300 Detection Based on EEG Shape Features
[{"from": "human", "value": "I am writing a research paper on a idea P300 Detection Based on EEG Shape Features"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a novel approach to describe a P300 by a shape-feature vector, which offers several advantages over the feature vector used by the BCI2000 system. Additionally,  present a calibration algorithm that reduces the dimensionality of the shape-feature vector, the number of trials, and the electrodes needed by a Brain Computer Interface to accurately detect P300s;  also define a method to find a template that best represents, for a given electrode, the subject\u2019s P300 based on his/her own acquired signals. Our experiments with 21 subjects shod that the SWLDA\u2019s performance using our shape-feature vector was , that is,  higher than the one obtained with BCI2000-feature\u2019s vector. The shape-feature vector is 34-dimensional for every electrode; hover, it is possible to significantly reduce its dimensionality while keeping a high sensitivity. The validation of the calibration algorithm shod an averaged area under the ROC (AUROC) curve of . Also, most of the subjects needed less than  trials to have an AUROC superior to . Finally,  found that the electrode C4 also leads to better classification."}]