A. spectral density(PSD) and time-frequency correlated features extraction techniques

A.

   Selection of Brain RegionsEEG signals are acquired from 21 electrodes, placing themon the scalp of human subjects. The EEG signals are recorded on a separatecomputer having 8 GB RAM with CPUclock of   Low Medium High Activation level Odor 1 (Perfume)         Odor 2 (Dettol)         Odor 3 (Acetic acid)         Odor 4 (Alchohol)  Fig.5. Section of brain region andfrequency bands from scalp maps of three concentration level of four stimuli3.4 GHz.Fig.

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5 shows the scalpmaps of one subject. Scalp maps of different concentration level of differentstimuli have been recorded during the experiment.From the scalp maps, it can be observed that pre-frontal, Frontal, parietal,and temporal lobes exhibit significant activations during the experiment. Here, the pre-frontal region is hardly activated inolfactorysensing and processing. Besides these, frontal and mainly parietal, temporalare found to take significantly active participation during the experiment. Thepaper 6 reveals that temporal lobe and frontal lobe are highly associatedwith human olfactory signal processing.

Therefore, we select F3, F4,F7, F8 and FZ (for frontal lobe) P3and P4 (from parietal lobe), T3 and T4(fromtemporal lobe) and FP1 and FP2 (for prefrontal lobe)forextracting necessary information using signal processing techniques.Fig.6shows the electrode positions over the scalp in ourbrain during experiment, where the red colored electrodes are used to collectdata. Electrodes are placed over the scalp using 10-20 electrode placementsystem 26.

Fig.6. Electrode position ofour experiment B.   EEG Feature Extraction and Feature SelectionFeatureextractionis very much important for EEG classification problem for accurate decoding ofmental tasks. There is variety of EEG feature extraction schemes: time-domainfeature extraction techniques like Hjorth parameters 18, Autoregressiveparameters 19 and also frequency-domain feature extraction techniques likepower spectral density(PSD) and time-frequency correlated features extractiontechniques likediscrete wavelet transform (DWT) 20.

We first plot the raw EEGsignal pattern recorded from the specific brain regions during the experiment.Fig. 7(a)showsthe plot of raw EEG signalscaptured from the temporal lobe duringthe smell stimuli presentation and Fig.

7(b)represents the filtered EEG signalsfor four different odor stimuli.Powerspectral density (PSD), is a well-known frequency-domain feature extractiontechnique to extract EEG signal power distribution. PSD is applied on thefiltered EEG signal acquired from prefrontal, frontal, parietal and temporallobe regions. Besides PSD, It is important to mention here that filtering ofEEG signal is done by using a standard Chebyshev 21 band pass infiniteimpulse response (IIR) filter of order 10, which has the pass band frequency of0.5-13 Hz. The selection is made so because of the superior performance ofChebyshev filter as compared to its standard counterparts including Butterworthand Elliptic filter. Now, for each subject and each vowel sound, PSD extract10×12×328 feature sets  (since, here,experiment is repeated 10 times and number of selected electrodes 12). Fig.

8(a)and Fig.8(b)present the PSD features extractedfrom the above brain regions.A.   Selection of Brain RegionsEEG signals are acquired from 21 electrodes, placing themon the scalp of human subjects. The EEG signals are recorded on a separatecomputer having 8 GB RAM with CPUclock of   Low Medium High Activation level Odor 1 (Perfume)         Odor 2 (Dettol)         Odor 3 (Acetic acid)         Odor 4 (Alchohol)  Fig.5. Section of brain region andfrequency bands from scalp maps of three concentration level of four stimuli3.4 GHz.

Fig. 5 shows the scalpmaps of one subject. Scalp maps of different concentration level of differentstimuli have been recorded during the experiment.From the scalp maps, it can be observed that pre-frontal, Frontal, parietal,and temporal lobes exhibit significant activations during the experiment. Here, the pre-frontal region is hardly activated inolfactorysensing and processing. Besides these, frontal and mainly parietal, temporalare found to take significantly active participation during the experiment. Thepaper 6 reveals that temporal lobe and frontal lobe are highly associatedwith human olfactory signal processing. Therefore, we select F3, F4,F7, F8 and FZ (for frontal lobe) P3and P4 (from parietal lobe), T3 and T4(fromtemporal lobe) and FP1 and FP2 (for prefrontal lobe)forextracting necessary information using signal processing techniques.

Fig.6shows the electrode positions over the scalp in ourbrain during experiment, where the red colored electrodes are used to collectdata. Electrodes are placed over the scalp using 10-20 electrode placementsystem 26.Fig.6.

Electrode position ofour experiment B.   EEG Feature Extraction and Feature SelectionFeatureextractionis very much important for EEG classification problem for accurate decoding ofmental tasks. There is variety of EEG feature extraction schemes: time-domainfeature extraction techniques like Hjorth parameters 18, Autoregressiveparameters 19 and also frequency-domain feature extraction techniques likepower spectral density(PSD) and time-frequency correlated features extractiontechniques likediscrete wavelet transform (DWT) 20. We first plot the raw EEGsignal pattern recorded from the specific brain regions during the experiment.Fig. 7(a)showsthe plot of raw EEG signalscaptured from the temporal lobe duringthe smell stimuli presentation and Fig.

7(b)represents the filtered EEG signalsfor four different odor stimuli.Powerspectral density (PSD), is a well-known frequency-domain feature extractiontechnique to extract EEG signal power distribution. PSD is applied on thefiltered EEG signal acquired from prefrontal, frontal, parietal and temporallobe regions. Besides PSD, It is important to mention here that filtering ofEEG signal is done by using a standard Chebyshev 21 band pass infiniteimpulse response (IIR) filter of order 10, which has the pass band frequency of0.5-13 Hz. The selection is made so because of the superior performance ofChebyshev filter as compared to its standard counterparts including Butterworthand Elliptic filter. Now, for each subject and each vowel sound, PSD extract10×12×328 feature sets  (since, here,experiment is repeated 10 times and number of selected electrodes 12). Fig.

8(a)and Fig.8(b)present the PSD features extractedfrom the above brain regions.A.   Selection of Brain RegionsEEG signals are acquired from 21 electrodes, placing themon the scalp of human subjects. The EEG signals are recorded on a separatecomputer having 8 GB RAM with CPUclock of   Low Medium High Activation level Odor 1 (Perfume)         Odor 2 (Dettol)         Odor 3 (Acetic acid)         Odor 4 (Alchohol)  Fig.5.

Section of brain region andfrequency bands from scalp maps of three concentration level of four stimuli3.4 GHz.Fig. 5 shows the scalpmaps of one subject. Scalp maps of different concentration level of differentstimuli have been recorded during the experiment.From the scalp maps, it can be observed that pre-frontal, Frontal, parietal,and temporal lobes exhibit significant activations during the experiment. Here, the pre-frontal region is hardly activated inolfactorysensing and processing.

Besides these, frontal and mainly parietal, temporalare found to take significantly active participation during the experiment. Thepaper 6 reveals that temporal lobe and frontal lobe are highly associatedwith human olfactory signal processing. Therefore, we select F3, F4,F7, F8 and FZ (for frontal lobe) P3and P4 (from parietal lobe), T3 and T4(fromtemporal lobe) and FP1 and FP2 (for prefrontal lobe)forextracting necessary information using signal processing techniques.Fig.6shows the electrode positions over the scalp in ourbrain during experiment, where the red colored electrodes are used to collectdata. Electrodes are placed over the scalp using 10-20 electrode placementsystem 26.

Fig.6. Electrode position ofour experiment B.   EEG Feature Extraction and Feature SelectionFeatureextractionis very much important for EEG classification problem for accurate decoding ofmental tasks. There is variety of EEG feature extraction schemes: time-domainfeature extraction techniques like Hjorth parameters 18, Autoregressiveparameters 19 and also frequency-domain feature extraction techniques likepower spectral density(PSD) and time-frequency correlated features extractiontechniques likediscrete wavelet transform (DWT) 20. We first plot the raw EEGsignal pattern recorded from the specific brain regions during the experiment.Fig.

7(a)showsthe plot of raw EEG signalscaptured from the temporal lobe duringthe smell stimuli presentation and Fig. 7(b)represents the filtered EEG signalsfor four different odor stimuli.Powerspectral density (PSD), is a well-known frequency-domain feature extractiontechnique to extract EEG signal power distribution. PSD is applied on thefiltered EEG signal acquired from prefrontal, frontal, parietal and temporallobe regions. Besides PSD, It is important to mention here that filtering ofEEG signal is done by using a standard Chebyshev 21 band pass infiniteimpulse response (IIR) filter of order 10, which has the pass band frequency of0.5-13 Hz. The selection is made so because of the superior performance ofChebyshev filter as compared to its standard counterparts including Butterworthand Elliptic filter. Now, for each subject and each vowel sound, PSD extract10×12×328 feature sets  (since, here,experiment is repeated 10 times and number of selected electrodes 12).

Fig.8(a)and Fig.8(b)present the PSD features extractedfrom the above brain regions.A.   Selection of Brain RegionsEEG signals are acquired from 21 electrodes, placing themon the scalp of human subjects. The EEG signals are recorded on a separatecomputer having 8 GB RAM with CPUclock of   Low Medium High Activation level Odor 1 (Perfume)         Odor 2 (Dettol)         Odor 3 (Acetic acid)         Odor 4 (Alchohol)  Fig.

5. Section of brain region andfrequency bands from scalp maps of three concentration level of four stimuli3.4 GHz.Fig. 5 shows the scalpmaps of one subject.

Scalp maps of different concentration level of differentstimuli have been recorded during the experiment.From the scalp maps, it can be observed that pre-frontal, Frontal, parietal,and temporal lobes exhibit significant activations during the experiment. Here, the pre-frontal region is hardly activated inolfactorysensing and processing. Besides these, frontal and mainly parietal, temporalare found to take significantly active participation during the experiment. Thepaper 6 reveals that temporal lobe and frontal lobe are highly associatedwith human olfactory signal processing.

Therefore, we select F3, F4,F7, F8 and FZ (for frontal lobe) P3and P4 (from parietal lobe), T3 and T4(fromtemporal lobe) and FP1 and FP2 (for prefrontal lobe)forextracting necessary information using signal processing techniques.Fig.6shows the electrode positions over the scalp in ourbrain during experiment, where the red colored electrodes are used to collectdata.

Electrodes are placed over the scalp using 10-20 electrode placementsystem 26.Fig.6. Electrode position ofour experiment B.   EEG Feature Extraction and Feature SelectionFeatureextractionis very much important for EEG classification problem for accurate decoding ofmental tasks.

There is variety of EEG feature extraction schemes: time-domainfeature extraction techniques like Hjorth parameters 18, Autoregressiveparameters 19 and also frequency-domain feature extraction techniques likepower spectral density(PSD) and time-frequency correlated features extractiontechniques likediscrete wavelet transform (DWT) 20. We first plot the raw EEGsignal pattern recorded from the specific brain regions during the experiment.Fig. 7(a)showsthe plot of raw EEG signalscaptured from the temporal lobe duringthe smell stimuli presentation and Fig. 7(b)represents the filtered EEG signalsfor four different odor stimuli.Powerspectral density (PSD), is a well-known frequency-domain feature extractiontechnique to extract EEG signal power distribution.

PSD is applied on thefiltered EEG signal acquired from prefrontal, frontal, parietal and temporallobe regions. Besides PSD, It is important to mention here that filtering ofEEG signal is done by using a standard Chebyshev 21 band pass infiniteimpulse response (IIR) filter of order 10, which has the pass band frequency of0.5-13 Hz. The selection is made so because of the superior performance ofChebyshev filter as compared to its standard counterparts including Butterworthand Elliptic filter. Now, for each subject and each vowel sound, PSD extract10×12×328 feature sets  (since, here,experiment is repeated 10 times and number of selected electrodes 12). Fig.8(a)and Fig.8(b)present the PSD features extractedfrom the above brain regions.