A method for determining a cognitive load of a subject from Electroencephalography (EEG) signals, the method comprising: receiving from a low resolution EEG device , by a processor , the EEG signals from a set of EEG channels associated with a left-frontal brain lobe, wherein the EEG signals are associated with the subject performing a cognitive task, wherein the low resolution EEG device is linked with a maximum of fourteen EEG channels; the method being characterized by the further steps of: preprocessing, by the processor , the EEG signals using a Hilbert-Huang Transform (HHT) filter that decomposes the EEG signals into empirical mode signals and remove a noise corresponding to one or more non-cerebral artifacts by replacing frequency of the EEG signals with a median value at a time point when the frequency of the EEG signals overshoots a predefined level at the time point, to generate preprocessed EEG signals, wherein the non-cerebral artifacts are originated from body parts and outside body parts, movement of the subject, settling of EEG electrodes, spikes originating from a momentary change in an impedance of the EEG electrode; extracting, by the processor , features comprising Fast Fourier Transform (FFT) based alpha and theta band power from the preprocessed EEG signals; generating, by the processor , a feature vector from the features; and classifying, by the processor, the feature vector using a supervised machine learning technique to determine the cognitive load of the subject. receiving from a low resolution EEG device , by a processor , the EEG signals from a set of EEG channels associated with a left-frontal brain lobe, wherein the EEG signals are associated with the subject performing a cognitive task, wherein the low resolution EEG device is linked with a maximum of fourteen EEG channels; the method being characterized by the further steps of: preprocessing, by the processor , the EEG signals using a Hilbert-Huang Transform (HHT) filter that decomposes the EEG signals into empirical mode signals and remove a noise corresponding to one or more non-cerebral artifacts by replacing frequency of the EEG signals with a median value at a time point when the frequency of the EEG signals overshoots a predefined level at the time point, to generate preprocessed EEG signals, wherein the non-cerebral artifacts are originated from body parts and outside body parts, movement of the subject, settling of EEG electrodes, spikes originating from a momentary change in an impedance of the EEG electrode; extracting, by the processor , features comprising Fast Fourier Transform (FFT) based alpha and theta band power from the preprocessed EEG signals; generating, by the processor , a feature vector from the features; and classifying, by the processor, the feature vector using a supervised machine learning technique to determine the cognitive load of the subject. preprocessing, by the processor , the EEG signals using a Hilbert-Huang Transform (HHT) filter that decomposes the EEG signals into empirical mode signals and remove a noise corresponding to one or more non-cerebral artifacts by replacing frequency of the EEG signals with a median value at a time point when the frequency of the EEG signals overshoots a predefined level at the time point, to generate preprocessed EEG signals, wherein the non-cerebral artifacts are originated from body parts and outside body parts, movement of the subject, settling of EEG electrodes, spikes originating from a momentary change in an impedance of the EEG electrode; extracting, by the processor , features comprising Fast Fourier Transform (FFT) based alpha and theta band power from the preprocessed EEG signals; generating, by the processor , a feature vector from the features; and classifying, by the processor, the feature vector using a supervised machine learning technique to determine the cognitive load of the subject. <EOS>