Page 243 - Artificial Intelligence in the Age of Neural Networks and Brain Computing

234 CHAPTER 11 Deep Learning Approaches to Electrophysiological

(B) For epochs standard the higher-level

s windows. over the (m), engineered compresses 20

5 averaged mean 228 ¼ 19 228:40:228) in features
nonoverlapping are TFRs the then, 12 (AE 1 , learned

N The and Therefore, 40
into subbands, TFR. autoencoder the
is partitioned computed. is 3 in whole the first compresses CJD-HC.

recording (TFR) subdivided for and The (C) (UL). 40:20:40) CJD-ENC,

EEG representation is TFR subbands (AE 2 , parameters (h 2 ). Finally, (D) depicts a classifier with a single hidden layer (h 3 ) of 10 neurons is trained (SL). The whole DL processor is possibly CJD-AD,
19-channels frequency averaged the for autoencoders autoencoder tasks:

The time Each both stacked second classification
[30]. (A) a channel). estimated two The the

Ref. channel, per are train to (h 1 ). of
proposed in EEG every (one TFRs (n) skewness used are and parameters 40 performance

the method for and averaged the and extracted in the improve

11.12 epoch, EEG 19 in (s), are representation to

FIGURE Flowchart of each resulting deviation features input fine-tuned