COMBUSTIÓN ELECTROQUÍMICA DE HIDRÓGENO

7.1 Discussion

Perspective of Applications

The results presented in this work have shown a very promising per­ spective for seizure prediction based upon the non-invasive approach. Although BSS has been of theoretical interest in seizure signal sep­ aration [52]- [55] [63], very few studies [42] have attem pted BSS for separation of EEGs for seizure prediction, because there are still many questions remaining in the application of BSS to brain signal separa­ tion. A part from the lim itation of ICA itself, the complexity of brain functions sometimes may make the results obtained from ICA difficult to be interpreted physiologically, which may lead to doubts about the accuracy of th e estim ated sources.

The work presented in this thesis has shown th a t as a consequence of applying a well developed separation algorithm, the predictability of seizure from the scalp EEG can be verified. The proposed CTICA algorithm has dem onstrated a b etter performance compared to other ICA methods. First, the TIC A model relaxes the assumption of in­ dependence and therefore is more suitable for brain signal separation. Second, CTICA introduces an averaged and band-limited reference sig­ 134

S e c tio n 7 .1 . D iscu ssion 135

nal, which further constrains th e TICA model based on the spatial and frequency domain properties of th e seizure signal. The reference sig­ nal can be constructed based on long tim e recorded EEGs, which can continuously capture the dynamic changes w ithin the epileptic brain. Third, by imposing the constraint, th e source closest to the reference can be obtained and the dynamic changes of th e source can be exploited in the prediction. The presented results have suggested a great poten­ tial in applying the CTICA to real application of seizure prediction.

Fusion of EEG and fMRI is a relatively new topic in research field. There are very lim ited studies [121] which have attem pted ICA to the combined EEG and fMRI. The results presented in this work have pro­ vided a stepping stone in th e forefront of this new field. For the first time, the tem poral inform ation from EEG has been incorporated into the spatial ICA model for fMRI analysis. This provides a new technique in which th e inform ation from EEG and fMRI can be fused through a mathem atical model. Not only this, the presented work also has es­ tablished a new m ethod for m apping of the spontaneous brain activity, which is the problem th a t can not be solved by using the popular ap­ proach of general linear model (GLM). The overall results may have more meaningful im pact on the methodology development for the hu­ man brain function m apping in neuroscience.

Limitations and F uture Work

First, the ICA model has its own limitations. Although the ambiguity of scaling can be overcome by d a ta preprocessing or imposing some con­ straints on th e sources and unmixing m atrix, the evaluation of the exact number of sources rem ains an open question in all applications of ICA,

S e c tio n 7 .1 . D iscu ssion 136

even though some m ethods have been suggested in the literature [8] - [16]. In the presented work, th e number of sources was assumed to be equal or less th an the num ber of sensors. The case when there are more sources th an sensors, is of theoretical and practical interest. Further studies on this subject may help to improve the proposed methods.

Second, the TICA model has some limitations. Based on this model, the nearby sources can be grouped together in the output. However, the results have shown th a t it can also group the artifact with the desired source if they are geometrically close to each other and active at the same time. This is one reason th a t when TICA is applied to the real EEG data, th e effect of grouping nearby sources might not be very ideal. The strategy to overcome this problem can be by developing a proper neighbourhood function based on the statistical properties of the desired source, by which th e artifacts and ideal source can be clustered in the different groups even they are close to each other.

Third, much more work needs to be done in fusion of EEG and fMRI. Further exploration of th e relation between EEG and fMRI, and devel­ oping more complex probabilistic models especially if larger d ata sets are available, may provide more solid foundation for combination for these two modalities. Further study may provide an engineering ground to fully exploit and illustrate the functional, anatomical, pathological, and physiological characteristics of the human brain.

In addition to these lim itations, it is also worthwhile to apply the CTICA to more real epileptic EEG d ata sets, therefore the robustness of the algorithm can be further investigated.

S e c tio n 7 .2 . C on clu sion s 137

7.2 Conclusions

In this thesis, the predictability of epileptic seizure based on the scalp EEGs has been investigated by applying BSS techniques and nonlinear analysis method. The proposed CTICA algorithm not only relaxed the assumption of independency of th e sources, but also constrained the TICA model based on the spatial and frequency domain properties of the seizure signal by using an averaged and band-limited reference signal. The results have dem onstrated th a t CTICA achieved a better separation performance for seizure EEGs th an other ICA methods. The results based on th e d a ta from three epileptic patients have shown th at the chaos measurem ent (using Lyapunov exponent) after application of the CTICA has similar tren d as th a t estim ated from the intracranial EEGs. Fusion of EEG and fMRI has also been studied. By applying a blind source extraction (BSE) algorithm, the effect of fMRI scanner artifacts are reduced effectively from simultaneously recorded EEGs. By introducing th e EEG as a tem poral constraint into the spatial ICA, the seizure BOLD has been detected effectively. The overall results from the present work have dem onstrated a very promising technique for seizure prediction using combined EEG and fMRI analysis.

APPENDIX: