  Representation and classification of Electroencephalography (EEG) brain
signals are critical processes for their analysis in cognitive tasks.
Particularly, extraction of discriminative features from raw EEG signals,
without any pre-processing, is a challenging task. Motivated by nuclear norm,
we observed that there is a significant difference between the variances of EEG
signals captured from the same brain region when a subject performs different
tasks. This observation lead us to use singular value decomposition for
computing dominant variances of EEG signals captured from a certain brain
region while performing a certain task and use them as features (nuclear
features). A simple and efficient class means based minimum distance classifier
(CMMDC) is enough to predict brain states. This approach results in the feature
space of significantly small dimension and gives equally good classification
results on clean as well as raw data. We validated the effectiveness and
robustness of the technique using four datasets of different tasks: fluid
intelligence clean data (FICD), fluid intelligence raw data (FIRD), memory
recall task (MRT), and eyes open / eyes closed task (EOEC). For each task, we
analyzed EEG signals over six (06) different brain regions with 8, 16, 20, 18,
18 and 100 electrodes. The nuclear features from frontal brain region gave the
100% prediction accuracy. The discriminant analysis of the nuclear features has
been conducted using intra-class and inter-class variations. Comparisons with
the state-of-the-art techniques showed the superiority of the proposed system.
