During the feature extraction thirty-five instantaneous power and fourteen neural synchronisation features were obtained for decoding movement and laterality for finger clicking events. As neural synchronisation features are related to movement laterality, therefore, only instantaneous power features were used to decode the movement (event and rest). In the subsequent laterality decoding, instantaneous power and/or neural synchronisation features were used. Both the movement and laterality decoding were performed through selection of optimal feature subset and then classification. Two classifiers, Bayesian and SVM were implemented for decoding of deep brain LFPs. The classifier was trained, validated and evaluated by a k-fold cross-validation procedure. Cross-validation provides a relatively unbiased estimate of the generalisation capability of the algorithm by splitting the dataset into k-subsets and selecting each subset as the test set with the remaining sets used to train the classifier. Using cross-validation, the LFP dataset from each subject was divided into training and testing sets. The training set was used for feature selection and it was performed in two phases. Features were firstly ranked according to their individual discriminability. Then based on ordered (high to low) ranked features, a subset of features were selected by evaluating the sequential feature selection (SFS) strategy as well as our newly developed method, the weighted sequential feature selection (WSFS) strategy based on individual feature contributions in second phase. In both strategies, a sequential forward subset selection approach was used. To rank the individual features, interclass separability based on the F-score ( ) and classification accuracy ( ) were used as a ranking criterion. The scores of ranking criteria, or for each individual feature were computed across training instances and ordered by ranking function, ( ). The detailed flowchart of both SFS and WSFS feature selection strategies to select optimal feature subset from available extracted features are presented in figure 6.8 and 6.9 respectively and detailed illustration of both strategies are also presented in section 6.3.2.4 and 6.3.2.5.
Figure 6.8: The flowchart for SFS feature selection strategy with three selection models (SFS filter (ISF), SFS wrapper (CA) and SFS wrapper (ISF-CA)) designed for selecting optimal feature subset for decoding movement from deep brain LFPs.
Figure 6.9: The flowchart for WSFS feature selection strategy with three selection models (WSFS embedded (ISF), SFS embedded (CA) and SFS embedded (ISF-CA)) designed for selecting optimal feature subset for decoding movement from deep brain
As discussed earlier (cf. 6.3.2), in the context of pattern classification and the construction principle of feature selection method, the SFS and WSFS strategy can be considered into three (filter, wrapper or embedded) taxonomical categories. Considering the taxonomical categories and feature ranking criterion ( or ), the SFS and WSFS feature selection strategy for LFP decoding organised into three alternative selection models to extract the optimal feature from the feature space. Thus three selection models for SFS organised as SFS filter (ISF), SFS wrapper (CA) and SFS wrapper (CA-ISF), while three selection models for WSFS organised as WSFS embedded (ISF), WSFS embedded (CA) and WSFS embedded (CA-ISF). In both SFS and WSFS strategy, the selection models are established based on how feature ranking and selection were incorporated to generate the optimal feature subset for providing the superior decoding performance.
The filter approach is computationally simple and fast, and it is independent of the classification methods. As feature ranking is a normalised measure between the features of two classes and the selection is based on its average score, which is independent of the classification methods. In other words, each feature in the feature space firstly ranked and ordered (high to low) based on their ISF score and then selected a subset of features whose ISF score is greater than the mean ISF score. Therefore, ISF feature ranking criterion incorporating subset selection based on mean ISF was used to generate optimal feature subset for classification modelled as SFS filter (ISF). The problem of the filter approach is that it ignores the interaction with classifier and it considers each feature separately, thereby ignores the feature dependences. The wrapper approach overcomes the problem of filter approach through interacting with the classifier. Therefore, feature ranking criterion incorporating sequential forward subset selection based on CA was used to generate optimal feature subset for classification modelled as SFS wrapper (CA). Similarly, ISF feature ranking criterion incorporating sequential forward subset selection based on CA to generate optimal feature subset for classification was modelled as SFS wrapper (ISF-CA). The common limitation of the wrapper approach is that it has higher risk of overfitting, i.e. it is biased towards the training set. Particularly, as SFS wrapper approaches are mainly based on the classification function for selecting optimal feature subset, when the training dataset changes, its performance varies, which is not robust for non-stationary signal dynamics,
such as those used in neural decoding. Again embedded approaches try to overcome the limitation of the wrapper approach by addressing the risk of overfitting.
Hence, to select the robust and optimal subset of features for deep brain LFP decoding, WSFS was introduced. The WSFS is considered as an embedded approach and it addresses the limitation of SFS wrapper approach by integrating the individual feature contributions through computing each feature’s weight with the SFS strategy. This strategy efficiently reduces the risk of overfitting and it has the ability to select the most effective features by reducing redundant features, which have least contribution to the classification, and also the selection process is independent of changes captured in the training set. Similar to the SFS filter and wrapper selection models, three WSFS embedded selection models were designed with integrating feature ranking ( or ) criteria. The feature ranking and subset selection (i.e. SFS filter (ISF)) incorporating WSFS strategy was used to generate an optimal subset of features for classification modelled as WSFS embedded (ISF). Again feature ranking and sequential forward subset selection based on CA (i.e. SFS wrapper (CA)) incorporating WSFS strategy was used to generate an optimal subset of features for classification modelled as WSFS embedded (CA). Similarly, feature ranking and sequential forward subset selection based on CA (i.e. SFS wrapper (ISF-CA)) incorporating WSFS strategy was used to generate an optimal subset of feature for classification modelled as WSFS embedded (ISF-CA). It is expected that WSFS based models will significantly improve the performance and robustness of the decoding process. In all three WSFS embedded models, the parameter was set to 50, i.e. five repeated 10-fold cross- validation (i.e. ) was used to evaluate WSFS for optimal feature subset selection.
Based on the selected LFP feature subset obtained by each of SFS and WSFS selection models (ISF, CA or ISF-CA), the Bayesian and SVM classifiers were separately trained. It is noted that during the feature selection in both SFS and WSFS, the respective classifier was also used for feature ranking and subset selection. The Bayesian classifier parameters were estimated with univariate Gaussian assumption. As mentioned earlier (cf. section 4.5), due to the limited size of the training dataset compared to the large number of features, univariate classifier performed better than multivariate case in this study. The SVM classifier parameters, C and were optimised to produce the highest
during the training stage. The decoding performance of the optimised Bayesian and SVM classifiers were evaluated through hundred repeated 10-fold cross-validation and the classification rate was calculated as an average. The performance of both classification methods in all SFS and WSFS selection models was evaluated and compared for decoding of voluntary movement and its laterality. It is noted that the decoding performances of movement laterality based on instantaneous power, neural synchronisation, and combining both features were also evaluated separately and compared for providing the better decoding performance based on the selected feature subset. To statistically compare the performances among the feature space (instantaneous power, neural synchronisation and combining both together), the selection models (ISF, CA and ISF-CA), the feature selection strategies (SFS and WSFS) as well as the classifiers (Bayesian and SVM) across all subjects, a repeated measure ANOVA was performed using SPSS (Ver. 15, Chicago, Illinois).
6.4 Results
In this study the decoding of voluntary movement activities was divided into two stages, namely classification between resting and voluntary movement, and then laterality classification between left and right hand finger clicking movements (figure 6.3(a)). Deep brain LFP data from twelve subjects (six PD and six dystonia subjects; deep brain LFP recorded from STN for five subjects and GPi for seven subjects; (cf. table 6.1)) were used for decoding the movement and its laterality. For movement and rest state classification only the instantaneous power based features were extracted for feature selection and classification. However for subsequent laterality, left and right, two types of features, instantaneous power and neural synchronisation were extracted. From the extracted features, feature selection and classification was evaluated for each type of feature separately as well as combining both features together to improve performance. The decoding performance was evaluated as averaged accuracy, sensitivity and specificity. Accuracy is defined as the percentage of correctly classified instances. Sensitivity is defined as the ratio of the number of true positives classified to the number of actual total positive cases. Specificity is defined as the ratio of the number of true negative classified to the number of actual total negative cases. The decoding performance of movement and laterality were evaluated for each subject separately and all subjects’ performance was averaged and presented as mean ± 1 SD. To identify the overall influence of the features for movement and laterality decoding in SFS and
WSFS feature selections, all selected features were recorded and averaged across all subjects in both classifiers (Bayesian and SVM) and all selection models (ISF, CA, ISF- CA).