Real-time brain computer interface using imaginary movements
Background Brain Computer Interface (BCI) is the method of transforming mental thoughts and imagination into actions. A real-time BCI system can improve the quality of life of patients with severe neuromuscular disorders by enabling them to communicate with the outside world. In this paper, the implementation of a 2-class real-time BCI system based on the event related desynchronization (ERD) of the sensorimotor rhythms (SMR) is described. Methods Off-line measurements were conducted on 12 healthy test subjects with 3 different feedback systems (cross, basket and bars). From the collected electroencephalogram (EEG) data, the optimum frequency bands for each of the subjects were determined first through an exhaustive search on 325 bandpass filters. The features were then extracted for the left and right hand imaginary movements using the Common Spatial Pattern (CSP) method. Subsequently, a Bayes linear classifier (BLC) was developed and used for signal classification. These three subject-specific settings were preserved for the on-line experiments with the same feedback systems. Results Six of the 12 subjects were qualified for the on-line experiments based on their high off-line classification accuracies (CAs > 75 %). The overall mean on-line accuracy was found to be 80%. Conclusions The subject-specific settings applied on the feedback systems have resulted in the development of a successful real-time BCI system with high accuracies..
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2015 |
|---|---|
| Erschienen: |
2015 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:3 |
|---|---|
| Enthalten in: |
EPJ nonlinear biomedical physics - 3(2015), 1 vom: 28. Okt. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
El-Madani, Ahmad [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| BKL: | |
|---|---|
| Themen: |
Bayes linear classifier (BLC) |
| Anmerkungen: |
© El-Madani et al. 2015 |
|---|
| doi: |
10.1140/epjnbp/s40366-015-0024-2 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
OLC2095031954 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2095031954 | ||
| 003 | DE-627 | ||
| 005 | 20240410084324.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 230331s2015 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1140/epjnbp/s40366-015-0024-2 |2 doi | |
| 035 | |a (DE-627)OLC2095031954 | ||
| 035 | |a (DE-He213)s40366-015-0024-2-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 570 |a 610 |q VZ |
| 084 | |a 44.31 |2 bkl | ||
| 100 | 1 | |a El-Madani, Ahmad |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Real-time brain computer interface using imaginary movements |
| 264 | 1 | |c 2015 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © El-Madani et al. 2015 | ||
| 520 | |a Background Brain Computer Interface (BCI) is the method of transforming mental thoughts and imagination into actions. A real-time BCI system can improve the quality of life of patients with severe neuromuscular disorders by enabling them to communicate with the outside world. In this paper, the implementation of a 2-class real-time BCI system based on the event related desynchronization (ERD) of the sensorimotor rhythms (SMR) is described. Methods Off-line measurements were conducted on 12 healthy test subjects with 3 different feedback systems (cross, basket and bars). From the collected electroencephalogram (EEG) data, the optimum frequency bands for each of the subjects were determined first through an exhaustive search on 325 bandpass filters. The features were then extracted for the left and right hand imaginary movements using the Common Spatial Pattern (CSP) method. Subsequently, a Bayes linear classifier (BLC) was developed and used for signal classification. These three subject-specific settings were preserved for the on-line experiments with the same feedback systems. Results Six of the 12 subjects were qualified for the on-line experiments based on their high off-line classification accuracies (CAs > 75 %). The overall mean on-line accuracy was found to be 80%. Conclusions The subject-specific settings applied on the feedback systems have resulted in the development of a successful real-time BCI system with high accuracies. | ||
| 650 | 4 | |a Brain computer interfaces (BCI) | |
| 650 | 4 | |a Electroencephalogram (EEG) | |
| 650 | 4 | |a Movement imagery (MI) | |
| 650 | 4 | |a Event-related desynchronization (ERD) | |
| 650 | 4 | |a Feedback systems | |
| 650 | 4 | |a Bayes linear classifier (BLC) | |
| 700 | 1 | |a Sorensen, Helge BD |4 aut | |
| 700 | 1 | |a Kjær, Troels W. |4 aut | |
| 700 | 1 | |a Thomsen, Carsten E. |4 aut | |
| 700 | 1 | |a Puthusserypady, Sadasivan |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t EPJ nonlinear biomedical physics |d Springer Berlin Heidelberg, 2013 |g 3(2015), 1 vom: 28. Okt. |h Online-Ressource |w (DE-627)752436678 |w (DE-600)2724361-8 |w (DE-576)391214861 |x 2195-0008 |7 nnns |
| 773 | 1 | 8 | |g volume:3 |g year:2015 |g number:1 |g day:28 |g month:10 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1140/epjnbp/s40366-015-0024-2 |z kostenfrei |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-PHA | ||
| 912 | |a GBV_ILN_20 | ||
| 912 | |a GBV_ILN_22 | ||
| 912 | |a GBV_ILN_23 | ||
| 912 | |a GBV_ILN_24 | ||
| 912 | |a GBV_ILN_31 | ||
| 912 | |a GBV_ILN_39 | ||
| 912 | |a GBV_ILN_40 | ||
| 912 | |a GBV_ILN_60 | ||
| 912 | |a GBV_ILN_62 | ||
| 912 | |a GBV_ILN_63 | ||
| 912 | |a GBV_ILN_65 | ||
| 912 | |a GBV_ILN_69 | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_73 | ||
| 912 | |a GBV_ILN_74 | ||
| 912 | |a GBV_ILN_95 | ||
| 912 | |a GBV_ILN_105 | ||
| 912 | |a GBV_ILN_110 | ||
| 912 | |a GBV_ILN_151 | ||
| 912 | |a GBV_ILN_161 | ||
| 912 | |a GBV_ILN_170 | ||
| 912 | |a GBV_ILN_206 | ||
| 912 | |a GBV_ILN_213 | ||
| 912 | |a GBV_ILN_230 | ||
| 912 | |a GBV_ILN_285 | ||
| 912 | |a GBV_ILN_293 | ||
| 912 | |a GBV_ILN_602 | ||
| 912 | |a GBV_ILN_2014 | ||
| 912 | |a GBV_ILN_2446 | ||
| 912 | |a GBV_ILN_4012 | ||
| 912 | |a GBV_ILN_4037 | ||
| 912 | |a GBV_ILN_4112 | ||
| 912 | |a GBV_ILN_4125 | ||
| 912 | |a GBV_ILN_4126 | ||
| 912 | |a GBV_ILN_4249 | ||
| 912 | |a GBV_ILN_4305 | ||
| 912 | |a GBV_ILN_4306 | ||
| 912 | |a GBV_ILN_4307 | ||
| 912 | |a GBV_ILN_4313 | ||
| 912 | |a GBV_ILN_4322 | ||
| 912 | |a GBV_ILN_4323 | ||
| 912 | |a GBV_ILN_4324 | ||
| 912 | |a GBV_ILN_4325 | ||
| 912 | |a GBV_ILN_4338 | ||
| 912 | |a GBV_ILN_4367 | ||
| 912 | |a GBV_ILN_4700 | ||
| 936 | b | k | |a 44.31 |j Medizinische Physik |j Medizinische Physik |q VZ |
| 951 | |a AR | ||
| 952 | |d 3 |j 2015 |e 1 |b 28 |c 10 | ||