3.5. A new method of real-time estimation of in-brain information of single-trial brain waves

A new method is proposed for estimating the information of single-trial brain waves in fine temporal structures with a cross-trial variability by using a set of standard brain waves in a given category for each individual. In the oscillatory brain waves recorded in layer III or I of the aPC of the isolated whole brain of a guinea pig, the wavelet correlation analysis provided a 75% accuracy for the first candidate and a > 92% probability of including the correct information for the two upper candidates (Tables 5 and 7). The results support the validity of the proposed criteria for selecting standard brain waves with a wide variation for estimating different information in a given category.

The accuracy of this method was not affected by the information redundancy of signal sources, such as those resulting from olfactory receptors with overlapping tuning specificities and an Standard brain

Second

Eighth

Second

Third

First

Fourth

First

Fifth

Second

Highest corr.

Lav

Lav

Lina

Lina

mc468

mc468

mc4

mc4

RN

waves

Second Lav

Eighth Lav

Second Lina

Third Lina First mc468 Fourth mc468

First mc4 Fifth mc4 Second RN Single-trial

First Lav

Third Lav Fourth Lav

Sixth Lav Seventh Lav

Ninth Lav

First Lina Fourth Lina Third mc468

Second mc4

Third mc4 Fourth mc4

Table 7.

Estimated information

 of single-trial

 brain waves in layer I of the aPC by ranking of wavelet correlations

 0.59

 0.65

 0.57

 0.67

 0.65

 0.79

 0.85

 0.70 Correct rate

 75%

 using two standard brain waves (set 1r). 35

 0.21

 mc4

 0.53

 0.779

 0.71

 0.64

 0.775

 0.778

 0.84

 0.80

 0.18

 mc4

 0.52

 0.62

 0.69

 0.77

 0.84

 0.891

 0.887

 0.64

 0.27

 mc468

 0.64

 0.55

 0.77

 0.837

 0.83

 0.89

 0.839

 0.51

 0.36

 mc468

 0.59

 0.40

 0.7758

 0.84

 0.85

 0.75

 0.7756 0.42

 0.27

 mc468

 0.73

 0.44

 0.78

 0.66

 0.69

 0.55

 0.54

 0.38

 0.63

 Lina

 0.56

 0.83

 0.62

 0.48

 0.68

 0.67

 0.72

 0.76

 0.28

 Lav

 0.56

 0.91

 0.45

 0.47

 0.53

 0.59

 0.64

 0.76

 0.06

 Lav

 0.81

 0.52

 0.79

 0.85

 0.78

 0.73

 0.75

 0.52

 0.37

 Lina

 0.83

 0.68

 0.58

 0.70

 0.66

 0.58

 0.61

 0.53

 0.20

 Lav

 0.79

 0.43

 0.74

 0.60

 0.72

 0.49

 0.51

 0.38

 0.48

 Lav

 0.77

 0.70

 0.66

 0.52

 0.59

 0.55

 0.63

 0.70

 0.43

 Lav

 brain waves

 0.47

 0.09

 0.48

 0.25

 0.35

 0.23

 0.30

 0.20

 1.00

—

Estimated

Second candidate

Third candidate

(>0.6)

(>0.6)

Lav Lina Lina

Lav mc4 mc4 Lav Lina mc4 mc4 mc4 mc468

100%

100%

mc4

Lav

mc468

http://dx.doi.org/10.5772/intechopen.74810

Lina

Lina

mc468

mc468

mc4

mc468

Lav

Wavelet Correlation Analysis for Quantifying Similarities and Real-Time Estimates of Information Encoded…

mc468

mc4

information

 0.42

 0.76

 0.49

 0.48

 0.54

 0.58

 0.69

 1.00

 0.20

 Lav

 0.59

 0.66

 0.76

 0.74

 0.81

 0.87

 1.00

 0.69

 0.30

 mc468

 0.56

 0.63

 0.68

 0.75

 0.79

 1.00

 0.87

 0.58

 0.23

 mc4

 0.63

 0.57

 0.85

 0.69

 1.00

 0.79

 0.81

 0.54

 0.35

 Lav

 0.73

 0.50

 0.67

 1.00

 0.69

 0.75

 0.74

 0.48

 0.25

 mc468

 0.64

 0.47

 1.00

 0.67

 0.85

 0.68

 0.76

 0.49

 0.48

 mc468

 0.56

 1.00

 0.47

 0.50

 0.57

 0.63

 0.66

 0.76

 0.09

 mc4

 1.00

 0.56

 0.64

 0.73

 0.63

 0.56

 0.59

 0.42

 0.47

 Lina


corresponding to the Set 1 of layer III (standard Set 1<sup>0</sup> in layer I), the accuracy of estimation was reduced by 17%, and the probability of including the correct information for the two upper candidates was reduced by 25% (to 75%) (data not shown). By using single standard brain waves (standard Set s1r), the accuracy and probability were slightly reduced compared to

Figure 9. Variation-dependent changes in the accuracy of estimated information of single-trial brain waves in layer III of

Finally, it was examined whether the combination of data for two recording sites (layers I and III) affected the accuracy for the first candidates. Using this method, the accuracy (75%) of estimated information was maintained but not improved in standard Set 1 + 1<sup>0</sup> and Set 1r + 1r'

3.5. A new method of real-time estimation of in-brain information of single-trial brain

A new method is proposed for estimating the information of single-trial brain waves in fine temporal structures with a cross-trial variability by using a set of standard brain waves in a given category for each individual. In the oscillatory brain waves recorded in layer III or I of the aPC of the isolated whole brain of a guinea pig, the wavelet correlation analysis provided a 75% accuracy for the first candidate and a > 92% probability of including the correct information for the two upper candidates (Tables 5 and 7). The results support the validity of the proposed criteria for selecting standard brain waves with a wide variation for estimating

The accuracy of this method was not affected by the information redundancy of signal sources, such as those resulting from olfactory receptors with overlapping tuning specificities and an

those of the standard Set s1 (data not shown).

different information in a given category.

(data not shown).

waves

the aPC.

34 Wavelet Theory and Its Applications

Wavelet Correlation Analysis for Quantifying Similarities and Real-Time Estimates of Information Encoded… http://dx.doi.org/10.5772/intechopen.74810 35


Table 8. Correct and error rates (e) of estimated information in single-trial brain waves recorded in layers I and III of the aPC by the wavelet correlation analysis.

experience dependency in layer I or from pyramidal cells with a stimulus dependency after the integration of signals from multiple cognate olfactory receptors in layer III (Table 8). Layer I brain waves comprising redundant signals exhibited a similar accuracy of estimated information and a slightly increased probability of including the correct information for the two upper candidates compared to layer III brain waves.

The signaling redundancy originates from an identical temporal profile of different subsets of neurons tuned to distinct or shared information or from identical temporal profiles that are composed of multiple different profiles of various different subsets of neurons tuned to multiple distinct or shared information. The constant error rates of mc468 for Lina between layers I and III (both ~25%, Table 8 and Figure 10) are likely attributable to the signaling redundancy rather than the information similarity or information redundancy. Moreover, in the increased case, there was a threefold higher error rate of Lav for mc4 in layer III than layer I, whereas the

Figure 10. Correct and error rates of estimated information in single-trial brain waves recorded in layers I and III of the

Wavelet Correlation Analysis for Quantifying Similarities and Real-Time Estimates of Information Encoded…

http://dx.doi.org/10.5772/intechopen.74810

37

error rates of Lav for Lina were almost constant between layers I and III.

aPC by the wavelet correlation analysis. These values are listed in Table 8.

wavelet correlation analysis

3.6. Applicable examples of estimated in-brain information in humans using the

Each brain system (e.g., a sensory, memory, decision, or motor system) is organized in a hierarchical manner from simple to complicated matters. The sensory system generates oscillatory activities between the related cortical regions and the thalamus, and the latter acts (except in the olfactory system) to gate the sensory input to the cortex and provides feedback from the cortical pyramidal neurons. In olfaction, transient oscillatory brain waves are observed in the aPC [5, 17–21]. Strong feed-forward inhibition [5, 22, 23] via the sensitive pathway from the olfactory bulb [24] and the other sensory thalamocortical circuit [25, 26] or higher olfactory centers [27] could induce oscillatory brain waves that would contribute to parts of the EEGs recorded at the respective positions on the human scalp, in analogy to these experimental animals. Such information-dependent temporal profiles of the EEGs may enable us to estimate in-brain information by comparison with a set of standard time-frequency

The redundancies of brain waves are attributable to two origins: information and signaling. In the olfactory system, the information redundancy changes through the signal pathway from the receptors to the higher cortical areas via signal integration in the third- or higher-order neurons and/or mutual inhibition [1, 11–13] for category [14] or elemental odor representation [15]. Unlike the >80% overlap of about 70 receptors for carvone enantiomers having similar odors [16], the quite different odors of Lav and mc468 evoked different amplitude receptor potentials in the olfactory epithelium and dissimilar brain waves in the anterior piriform cortex [1]. Nevertheless, the wavelet correlation analysis sometimes produced the highest correlation coefficients of Lav for mc468. The error rate of Lav for mc468 was 9.3% in layer I brain waves but was reduced to 1.7% in layer III brain waves (Table 8 and Figure 10), which is consistent with the change in the information redundancy from high to low stages between layers I and III. On the other hand, the error rate of mc468 for Lav was 0% in both layers I and III. For the single-compound odors, Lina and mc4 exhibited odor similarity-dependent changes in the error rates of the estimated information between layers I and III. The error rates of the single compounds for their original mixture odors (partially similar odor) increased between layers I and III (0 ! 7.7% in Lina and 13.6 ! 30.8% in mc4) and those of single compounds for their nonrelative mixture odors (dissimilar odor) decreased between layers I and III (46.7 ! 19.2% in Lina and 4.5 ! 0% in mc4). Notably, the error rates between these single compounds were 0% in both layers I and III. These results suggest a partial overlap of the elemental odors that are represented in the pyramidal cells in the aPC and are recorded in layer III as brain waves. The total error rates of Lina decreased in layer III compared to those of layer I (and vice versa for the correct rate), whereas those of mc4 increased.

Wavelet Correlation Analysis for Quantifying Similarities and Real-Time Estimates of Information Encoded… http://dx.doi.org/10.5772/intechopen.74810 37

Figure 10. Correct and error rates of estimated information in single-trial brain waves recorded in layers I and III of the aPC by the wavelet correlation analysis. These values are listed in Table 8.

experience dependency in layer I or from pyramidal cells with a stimulus dependency after the integration of signals from multiple cognate olfactory receptors in layer III (Table 8). Layer I brain waves comprising redundant signals exhibited a similar accuracy of estimated information and a slightly increased probability of including the correct information for the two upper

Table 8. Correct and error rates (e) of estimated information in single-trial brain waves recorded in layers I and III of the

Lav Layer I (input) 62.8% 20.9% (e) 9.3% (e) 7.0% (e)

Lina Layer I (input) 0.0% (e) 53.3% 46.7% (e) 0.0% (e)

mc468 Layer I (input) 0.0% (e) 25.0% (e) 75.0% 0.0% (e)

mc4 Layer I (input) 4.5% (e) 0.0% (e) 13.6% (e) 81.8%

Layer III (output) 57.9% 18.4% (e) 1.3% (e) 22.4% (e)

Layer III (output) 7.7% (e) 73.1% 19.2% (e) 0.0% (e)

Layer III (output) 0.0% (e) 26.7% (e) 73.3% 0.0% (e)

Layer III (output) 0.0% (e) 0.0% (e) 30.8% (e) 69.2%

Lav Lina mc468 mc4

The redundancies of brain waves are attributable to two origins: information and signaling. In the olfactory system, the information redundancy changes through the signal pathway from the receptors to the higher cortical areas via signal integration in the third- or higher-order neurons and/or mutual inhibition [1, 11–13] for category [14] or elemental odor representation [15]. Unlike the >80% overlap of about 70 receptors for carvone enantiomers having similar odors [16], the quite different odors of Lav and mc468 evoked different amplitude receptor potentials in the olfactory epithelium and dissimilar brain waves in the anterior piriform cortex [1]. Nevertheless, the wavelet correlation analysis sometimes produced the highest correlation coefficients of Lav for mc468. The error rate of Lav for mc468 was 9.3% in layer I brain waves but was reduced to 1.7% in layer III brain waves (Table 8 and Figure 10), which is consistent with the change in the information redundancy from high to low stages between layers I and III. On the other hand, the error rate of mc468 for Lav was 0% in both layers I and III. For the single-compound odors, Lina and mc4 exhibited odor similarity-dependent changes in the error rates of the estimated information between layers I and III. The error rates of the single compounds for their original mixture odors (partially similar odor) increased between layers I and III (0 ! 7.7% in Lina and 13.6 ! 30.8% in mc4) and those of single compounds for their nonrelative mixture odors (dissimilar odor) decreased between layers I and III (46.7 ! 19.2% in Lina and 4.5 ! 0% in mc4). Notably, the error rates between these single compounds were 0% in both layers I and III. These results suggest a partial overlap of the elemental odors that are represented in the pyramidal cells in the aPC and are recorded in layer III as brain waves. The total error rates of Lina decreased in layer III compared to those of layer I (and vice versa

candidates compared to layer III brain waves.

aPC by the wavelet correlation analysis.

36 Wavelet Theory and Its Applications

Information Recoding sites Estimated information

for the correct rate), whereas those of mc4 increased.

The signaling redundancy originates from an identical temporal profile of different subsets of neurons tuned to distinct or shared information or from identical temporal profiles that are composed of multiple different profiles of various different subsets of neurons tuned to multiple distinct or shared information. The constant error rates of mc468 for Lina between layers I and III (both ~25%, Table 8 and Figure 10) are likely attributable to the signaling redundancy rather than the information similarity or information redundancy. Moreover, in the increased case, there was a threefold higher error rate of Lav for mc4 in layer III than layer I, whereas the error rates of Lav for Lina were almost constant between layers I and III.

#### 3.6. Applicable examples of estimated in-brain information in humans using the wavelet correlation analysis

Each brain system (e.g., a sensory, memory, decision, or motor system) is organized in a hierarchical manner from simple to complicated matters. The sensory system generates oscillatory activities between the related cortical regions and the thalamus, and the latter acts (except in the olfactory system) to gate the sensory input to the cortex and provides feedback from the cortical pyramidal neurons. In olfaction, transient oscillatory brain waves are observed in the aPC [5, 17–21]. Strong feed-forward inhibition [5, 22, 23] via the sensitive pathway from the olfactory bulb [24] and the other sensory thalamocortical circuit [25, 26] or higher olfactory centers [27] could induce oscillatory brain waves that would contribute to parts of the EEGs recorded at the respective positions on the human scalp, in analogy to these experimental animals. Such information-dependent temporal profiles of the EEGs may enable us to estimate in-brain information by comparison with a set of standard time-frequency power profiles of EEGs in each individual. To this aim, a wavelet correlation analysis of the brain waves in a guinea pig was conducted using standard brain waves with the proposed criteria and achieved an accuracy of 75% for the first candidates. This accuracy is attributable to the comparisons with standard single-trial responses in the wavelet time-frequency power profiles.

nonolfactory origin-free brain waves. We are also grateful to Kiyo Murano for writing the computer software for wavelet transformation. This work was supported by grants (T.S.) from METI, Japan, and Grant-in-Aids for Scientific Research (B) #15H02730 (T.S.) from the MEXT, Japan.

Wavelet Correlation Analysis for Quantifying Similarities and Real-Time Estimates of Information Encoded…

http://dx.doi.org/10.5772/intechopen.74810

39

, Ichiro Takashima<sup>3</sup> and Toshio Iijima4

1 Biomedical Research Institute, National Institute of Advanced Industrial Science and

[1] Sato T, Kajiwara R, Takashima I, Iijima T. A novel method for quantifying similarities between oscillatory neural responses in wavelet time-frequency power profiles. Brain

[2] Myrden A, Chau T. A passive EEG-BCI for single-trial detection of changes in mental state. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017;25:345-356

2 School of Science and Technology, Meiji University, Kawasaki, Japan

4 Graduate School of Life Sciences, Tohoku University, Sendai, Japan

Research. 2016;1636:107-117. DOI: 10.1016/j.brainres.2016.01.054

3 Human Technology Research Institute, AIST, Tsukuba, Japan

Abbreviations

aPC anterior piriform cortex

EEG electroencephalography

EOG electro-olfactogram

LFP local field potential LOT lateral olfactory tract

OR olfactory receptor

Technology (AIST), Ikeda, Japan

Author details

Takaaki Sato<sup>1</sup>

References

osci-LFP oscillatory local field potential

\*, Riichi Kajiwara2

\*Address all correspondence to: taka-sato@aist.go.jp

FFT fast Fourier transform

aPCvr ventro-rostral region of the aPC

Conventional methods have focused only on some parts of the brain wave characteristics. For example, the FFT power spectra of sensorimotor EEGs [28, 29] or auditory EEGs [30] in specific frequency bands at a specific recording position were analyzed for the development of brain-computer interfaces. The Morlet wavelet convolutions for four-frequency band powers of the single-trial EEGs were analyzed to understand the cognitive control system via a priori estimation of information across three tasks [31]. By using the wavelet correlation analysis in the time-frequency power profiles at nine frequencies, these analyses could be improved in their subprocesses. Odor sensation [32, 33] and color-opponent responses [34] were also recorded in humans at Fz and an intermediate position between Oz and the inion, respectively, and they demonstrated informational differences in response amplitudes or profiles. Like EEGs in object recognition and those responsible for mental states, these EEGs are also subjects for the application of the wavelet correlation analysis for estimating in-brain fine information. Pain-related alpha-band desynchronization at contralateral-central electrodes (C2, C4, CP2, and CP4) and gamma-band synchronization at the ipsilateral-posterior electrodes (P3, P5, and so on) [35] are also good candidates for application. In animal models, the neural pathways of innate and learned fear responses have been revealed [36], and different pathways of stress relaxation using rose and hinokitiol odors were found [37, 38]. Therefore, determining their differing time-frequency power profiles would enable us to estimate the strengths of stress or relaxation in EEGs in humans. Future studies will focus on programming the wavelet correlation analysis for real-time estimates of in-brain information in humans.

#### 4. Conclusions

We developed a new method for a similarity analysis and real-time estimates of in-brain information in single-trial brain waves by ranking the correlation coefficients in the wavelet correlation analysis. The wavelet correlation analysis with a set of standard brain waves provided the first candidate of estimated information with an accuracy of 75% with a > 92% probability of including the correct information for the two upper candidates, regardless of the information redundancy of signal sources. This method may be also useful for its applications to brain-machine interfaces or medical/research tools.

#### Acknowledgements

We would like to thank Dr. Mutsumi Matsukawa for his contributions to the development of the isolated whole-brain experimental system that enabled the recordings of odor-induced and nonolfactory origin-free brain waves. We are also grateful to Kiyo Murano for writing the computer software for wavelet transformation. This work was supported by grants (T.S.) from METI, Japan, and Grant-in-Aids for Scientific Research (B) #15H02730 (T.S.) from the MEXT, Japan.
