The code provides offline aclassification algorithms for the provided benchmark dataset [Wang2017]. Imlpemented are:
- CCA: simple CCA based classification [Lin2007]
- FBCCA: Ensamble classifier using frequency subbands to increase contribution by harmonics [Chen2015a]
- Extended CCA: Ensamlbe classifier based approach taking subject specific training data into account [Chen2014]
- Extended FBCCA: combination of FBCCA and extended CCA [Chen2015b]
Further, the Repository contains shell scripts to run the files on the HPC (Supercomputer) at DTU. These scipts take variables as well, to simplify running with different parameters.
bsub -env length=5,subjects=35,tag=filt < submit_cca.sh;
bsub -env length=5,subjects=35,tag=filt < submit_ext_cca.sh;
bsub -env length=5,subjects=35,tag=filt < submit_fbcca.sh;
bsub -env length=5,subjects=35,tag=filt < submit_ext_fbcca.sh;
bstat
Additionally, all scripts can be called with command-line commands:
run cca.py --length 5 --subjects 35 --tag test
run fbcca.py --length 5 --subjects 35 --tag test
run advanced_cca.py --length 5 --subjects 35 --tag test
run advanced_fbcca.py --length 5 --subjects 35 --tag test
// evaluation script
run evaluation --length 5 --subjects 35 --tag test
functions.py contains all functions that are used multiple times and has to be imported at the beginning of all other files.
Files are stored in following structure:
PBCI/
|
├── results/ # all the outputs/results from 1.-4.
│
├── data/ # subject data from benchmar data set
|
├── figures/ # resulting figures from evaluation
|
└── log/ # output from cluster
Make sure that all these folder exists and downlad the data from here.
[Lin2007] Z. Lin, C. Zhang, W. Wu, and X. Gao, “Frequency recognitionbased on canonical correlation analysis for SSVEP-Based BCIs,”IEEE Transactions on Biomedical Engineering, vol. 54, no. 6,pp. 1172–1176, 2007,ISSN: 00189294
[Chen2014] X. Chen, Y. Wang, M. Nakanishi, T. P. Jung, and X. Gao, “Hy-brid frequency and phase coding for a high-speed SSVEP-basedBCI speller,”2014 36th Annual International Conference of theIEEE Engineering in Medicine and Biology Society, EMBC 2014,pp. 3993–3996, 2014.
[Chen2015a] X. Chen, Y. Wang, S. Gao, T. P. Jung, and X. Gao, “Filterbank canonical correlation analysis for implementing a high-speedSSVEP-based brain-computer interface,”Journal of Neural Engi-neering, vol. 12, no. 4, 2015,ISSN: 17412552
[Chen2015b] X. Chen, Y. Wang, M. Nakanishi, X. Gao, T. P. Jung, and S. Gao,“High-speed spelling with a noninvasive brain-computer interface,”Proceedings of the National Academy of Sciences of the United Statesof America, vol. 112, no. 44, E6058–E6067, 2015,ISSN: 10916490
[Wang2017] Y. Wang, X. Chen, X. Gao, and S. Gao, “A Benchmark Datasetfor SSVEP-Based Brain-Computer Interfaces,”IEEE Transactionson Neural Systems and Rehabilitation Engineering, vol. 25, no. 10,pp. 1746–1752, 2017,ISSN: 15344320