Braedon J Davey, Daniel Lindsay, Justin Cousins, Beverley D Glass
Pharmacy Students' Perspective on Business Management for the Community Pharmacist
American journal of pharmaceutical education
objectiverecently demand wearable devices using electroencephalography eeg increased rapidly many fields due volume computation constraints wearable devices usually compress transmit eeg external devices analysis however current eeg compression algorithms tailormade wearable devices limited computing storage firstly huge amount parameters makes difficult apply wearable devices secondly tricky learn eeg signals distribution law due low signaltonoise ratio leads excessive reconstruction error suboptimal compression performanceapproachhere feature enhanced asymmetric encodingdecoding network proposed eeg encoded lightweight model subsequently decoded multilevel feature fusion network extracting encoded features deeply reconstructing signal twobranch structuremain resultson public eeg datasets motor imagery eventrelated potentials experimental results show proposed method achieved state art compression performance addition neural representation analysis classification performance reconstructed eeg signals also show method tends retain taskrelated information compression ratio increases retains reliable discriminative information eeg compressionsignificancethis paper tailors asymmetric eeg compression method wearable devices achieves stateoftheart compression performance lightweight manner paving way application eegbased wearable devices