Does feedback modality affect performance of brain computer interfaces?
| Autor: | Mathias Baumert, Derek Abbott, Sam Darvishi, Michael C. Ridding |
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| Rok vydání: | 2015 |
| Předmět: |
Information transfer
Modality (human–computer interaction) medicine.diagnostic_test Computer science business.industry Speech recognition Electroencephalography Machine learning computer.software_genre Motor imagery Autoregressive model medicine Artificial intelligence Motor learning business computer Motor skill Brain–computer interface |
| Zdroj: | NER |
| Popis: | Brain computer interfaces (BCI) are used for communication and rehabilitation. One of the main categories of BCI techniques is motor imagery based BCI (MI-BCI). A large number of studies have focused on machine learning approaches to optimize MI-BCI performance. However, enhancement of MI-BCI through provision of optimized feedback modalities has not received equal attention. Motor imagery and motor execution activate almost the same area of the brain. During motor skills performance, a combination of proprioceptive and direct visual feedback (PDVF) is provided. Thus, we hypothesized that MI-BCI that receives PDVF outperforms the traditional MI-BCI, which only uses indirect visual feedback (IVF). We studied 8 healthy subjects performing MI through (i) IVF and (ii) PDVF. We used 8 channel electroencephalogram (EEG) signals and extracted features using an autoregressive model and classified MIs using linear regression. On average, PDVF increased the accuracy of MI performance by 11%, and improved information transfer rate (ITR) by more than two times. In conclusion, using PDVF appears to improve MI-BCI performance according to the studied metrics, making this approach potentially more reliable. |
| Databáze: | OpenAIRE |
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