Name
Abstract | ||
|---|---|---|
In order to increase the classification accuracy of the mental tasks with speech imagery, a time-frequency-space range selection model based on neighborhood mutual information (NMI) is proposed. According to time, the electroencephalography (EEG) signals are divided into 7 distinct segments. These 7 sections of signals are filtered by 28 band pass filters with different frequency range. The filtered signals are extracted by common spatial pattern (CSP) to obtain spatial matrices. Then, the NMI values of these matrices are calculated. At last, the time-frequency-space range is optimized by NMI values. The EEG signals are processed by the selected time-frequency-space range, and the eigenvalues are calculated and classified by variance and support vector machines, respectively. From the results of 10 subjects, the average classification accuracy is improved by 3.0% after optimization. The improvements of subjects S2 and S5 are the most pronounced, and their results are increased by 5.0% and 5.2%, respectively. With automatic range selection and improvement of classification results, the model is entirely applicable to real time optimization calculation of online brain-computer interfaces. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3239264.3239281 | ICBBS |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li Wang | 1 | 250 | 56.88 |
| Chun Zhang | 2 | 0 | 0.68 |
| Hu Xiaodong | 3 | 347 | 38.23 |