LSTM based GAN Networks for Enhancing Ternary Task Classification Using fNIRS Data

Brain activation patterns vary according to the tasks performed by the subject. Neuroimaging techniques can be used to map the functioning of the cortex to capture brain activation patterns. Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique increasingly used for task classification based on brain activation patterns. fNIRS can be widely used in population studies due to the technology's economic,non-invasive, and portable nature. The multidimensional and complex nature of fNIRS data makes it ideal for deep learning algorithms for classification. Most deep learning algorithms need a large amount of data to be appropriately trained. Generative networks can be used in such cases where a substantial amount of data is required. Still, the collection is complex due to various constraints. Conditional Generative Adversarial Networks (CGAN) can generate artificial samples of a specific category to improve the deep learning classifier's accuracy when the sample size is insufficient. The proposed system uses an LSTM based CGAN with an LSTM classifier to enhance the accuracy through data augmentation. The system can determine whether the subject's task is a Left Finger Tap, Right Finger Tap, or Foot Tap based on the fNIRS data patterns. The authors obtained a task classification accuracy of 90.2% for the LSTM based GAN combination.Clinical relevance- Acquiring medical data present practical difficulties due to time, money, labor, and economic cost. The deep learning-based model can better perform medical image classification than hand-crafted features when dealing with many data. GAN-based networks can be valuable in the medical field where collecting extensive data is not feasible. GAN-generated synthetic data can be used to improve the classification accuracy of classification systems.