Sample size / Subjects& Stimulus
Frequency Range
Stimulus Levels Analysis Method/ Feature
Pieter-Jan Kindermans,,
10 Samples & 125 ms 256Hz 300ms, post stimulus ERP Features High efficient Unsupervised P300 speller BCI has been presented
Dandan Huang,
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,,
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,,
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.
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,,
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,,
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,,
(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,,
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,,
3 subjects 1000Hz Not Applicable AR feature extraction Model Real-Time BCI- System Based on Motor Imagery is reported with the best accuracy levels.