|
Authors (Year) |
Sample size / Subjects& Stimulus Duration |
Stimuli Frequency Range (Hz) |
Stimulus Levels |
Analysis Method/ Feature Extraction |
Reports/Results |
|---|---|---|---|---|---|
| Pieter-Jan Kindermans, et.al, (2014) |
10 Samples & 125 ms | 256Hz | 300ms, post stimulus | ERP Features | High efficient Unsupervised P300 speller BCI has been presented |
| Dandan Huang, (2012) |
5 Healthy Subjects (2 out 5 Subjects got 200 samples) |
256Hz | Not Applicable | Spatio-Temporal Features | High-Performance 2D BCI wheelchair has been Reported, with an average classification accuracy of 70% to 80% |
| Eric C Leuthardt, et.al, (2006) |
4 subjects 3male & 1 female |
180Hz | 12-time intervals, post-stimulus | ECoG Features (Amplitude in specific Frequency bands) | ECoG based BCI scheme is more efficient than EEG based BCIs. All the four controls achieved 73% to 100% of performance efficiency, but this method is invasive. |
| Damien Coyle, et.al, (2005) |
3 healthy Subjects | 128Hz | Not Applicable | Self-Organizing Fuzzy Neural Network-based Time Series Prediction (Statistical Time Features) |
High Efficient classification accuracy, Information transfer rate (ITR) and Mutual Information (MI) rate have been achieved. |
| YaninaAtum, et.al, (2010) |
Single Subject | 1024Hz | Not Applicable | Discrete Dictionary-based Feature Extraction Approach | The wrapped wavelet samples represent the best performance over the temporal patterns. |
| Pieter-Jan Kindermans, et.al, (2012) |
Akimpech P300 database, which covers 22 subjects performing Spanish Language Spelling | 256Hz | 300ms, post-stimulus | ERP Features | Results show Unsupervised P300 speller models perform better than supervised models in specific areas. |
| Xiaogang Chen, et.al, (2012) |
12 subjects | Not mentioned | Stimulation Levels varies between each subject from (o – 1s) | SSVEP Features | High performance, High speedSpellerBCI for communication has been reported, with high-speed information transfer rate of 5.32 bps |
| Masaki Nakanishi, et.al, (In Press) |
13 Subjects | Not mentioned | Visual Stimulus is used, Stimulus levels Not seen |
SSVEP (time-domain) Features | High-Speed SSVEP- BCI in real timeapplications. Achieved an average of 166.91 bits/min for Information Transfer Rate. |
| C. Guger, et.al, (2009) |
100 subjects 32 female and 68 male |
256Hz | 800ms, Post Stimulus | Features are not apparently seen but LDA is used to choose the accurate features. | High accuracy has been reported for the persons with motor disabilities, with a spelling accuracy of 80% to 100% |
| Tie-Jun Liu, et.al, (2009) |
3 subjects | 1000Hz | Not Applicable | AR feature extraction Model | Real-Time BCI- System Based on Motor Imagery is reported with the best accuracy levels. |