A method for determining a cognitive load of a person using a modified baseline, the method comprising a processor implemented steps of: instructing the person to stay in an eye closed rest state, stay in a baseline state and carry out a trial state respectively, wherein, a task is performed by the person during the trial state; sensing an EEG signal of the person using an EEG sensor ; sensing a GSR signal of the person using a GSR sensor ; preprocessing the EEG signal and the GSR signal using a preprocessor; segmenting the preprocessed EEG signal and the preprocessed GSR signal in to a rest interval corresponding to the rest state, a baseline interval corresponding to the baseline state and a trial interval corresponding to the trial state; performing power spectral analysis of the EEG signal to determine a first set of inactive states of the person from the baseline interval and the rest interval; identifying a most inactive window out of the first set of inactive states, wherein the most inactive window is during the rest interval of the person; determining the cognitive load of the person using the EEG signal of the person during the trial state and by using the most inactive window as the modified baseline; and validating the identification of a first inactive window wherein the validating comprises; performing power spectral analysis of the GSR signal to determine a second set of inactive states of the person from the baseline interval and the rest interval; identifying a most inactive window out of the second set of inactive states, wherein the most inactive window is during the rest interval of the person; and validating the most inactive state derived from the EEG signal with respect to the most inactive state derived from the GSR signal. instructing the person to stay in an eye closed rest state, stay in a baseline state and carry out a trial state respectively, wherein, a task is performed by the person during the trial state; sensing an EEG signal of the person using an EEG sensor ; sensing a GSR signal of the person using a GSR sensor ; preprocessing the EEG signal and the GSR signal using a preprocessor; segmenting the preprocessed EEG signal and the preprocessed GSR signal in to a rest interval corresponding to the rest state, a baseline interval corresponding to the baseline state and a trial interval corresponding to the trial state; performing power spectral analysis of the EEG signal to determine a first set of inactive states of the person from the baseline interval and the rest interval; identifying a most inactive window out of the first set of inactive states, wherein the most inactive window is during the rest interval of the person; determining the cognitive load of the person using the EEG signal of the person during the trial state and by using the most inactive window as the modified baseline; and validating the identification of a first inactive window wherein the validating comprises; performing power spectral analysis of the GSR signal to determine a second set of inactive states of the person from the baseline interval and the rest interval; identifying a most inactive window out of the second set of inactive states, wherein the most inactive window is during the rest interval of the person; and validating the most inactive state derived from the EEG signal with respect to the most inactive state derived from the GSR signal. performing power spectral analysis of the GSR signal to determine a second set of inactive states of the person from the baseline interval and the rest interval; identifying a most inactive window out of the second set of inactive states, wherein the most inactive window is during the rest interval of the person; and validating the most inactive state derived from the EEG signal with respect to the most inactive state derived from the GSR signal. <EOS>