Towards Reliable Brain-Computer Interface: Achieving Perfect Accuracy by Sacrificing Time

Aju-arvuti liides (AAL) on süsteem aju elektriliste impulside väljavõtmiseks janende kasutamiseks arvuti tarkvara juhtimiseks. AAL opereerimiseks peab kasutaja kontsentreeruma mingile mõttelisele ülesandele. Lisaks impulside mõõtmisele muudab AAL elekroonilisi signaale digitaalseks ja selle järgi tuvastab vastava arvuti käsu. Kahjuks on õige käsu tuvastamise tõenäosus alati alla 100%, mistõttu AAL süsteemide tõhusus on võrdlemisi madal.Madal tõhusus on AAL-i jaoks suureks probleemiks, sest senikaua kuni needsüsteemid pakuvad madalaid tuvastamise täpsuseid, jäävad need paljudes valdkondades ilma kasutamiseta. Antud probleemi lahendamiseks enamasti üritatakse tõsta AAL-i täpsust ühe kontsentreerumiskatse raames ja ei pöörata tähelepanu kontsentreerumiskatse kestvusele. Meie lähenemine aga põhineb arusaamisel, kui palju kontsentreerumiskatseid on vaja kasutajal järjest teostada (s.t kui kaua aega on nõutud), et saavutada 99% täpsus.Selles töös kirjeldatud lahendus põhineb Condorcet kohtu teoreemil [1]. Teoreem väidab, et kui on olemas kaks valikuvõimalust ja tõenäosus valida õiget on suurem kui 50%, kui me teostame mitu valimiskatset järjest, siis tõenäosus, et valitakse õiget valikut tõuseb iga järgneva valimiskatsega. Antud töös rakendasime põhilist Condorcet printsiipi aju-arvuti liidesele. Kõigepealt me arendame süsteemi, mis on suuteline saavutama ühe mõttelise ülesande kontsentreerumiskatse täpsuseks rohkem kui 50% ja seejärel proovime läbi mitme kontsentreerumiskatse parandada keskmist täpsust. Me eeldame, et kui kasutada piisavat kogust kontsentreerumiskatseid, siis me jõuame 99% klassifitseerimistäpsuseni. Me võrdleme teoreetilisi tulemusi eksperimentaalsetega ning arutleme nende üle. AAL tehnoloogia on võrdlemisi uus valdkond. Selle tehnoloogia täielik toomine meie igapäevaellu nõuab tugevat panust teadlastelt ja inseneridelt, et muuta AAL usaldusväärseks süsteemiks. Antud töö eesmärk on panustada AAL süsteemi kindlusesse.Brain-computer interface (BCI) is a computer system for extracting brain electricneural signals and using them to control computer applications. For the operationBCI requires a user to concentrate on some mental tasks. Besides measuringthe signals, BCI converts raw electric signal to digital representation and maps thedata to computer commands. Unfortunately, the probability of predicting the rightcommand is below 100% and therefore the reliability of these systems is relativelylow.Low reliability is a huge problem for BCI, since they will not be widely trustedand used while the prediction accuracy is low. The existing solutions usually tryto improve the prediction accuracy of BCI without focusing too much on the timewhat is required for a single user’s concentration attempt. They apply differentprediction models and signal processing techniques in order to raise the accuracyof prediction. Our solution goes the opposite way – it tries to discover how manyconcentration attempts should be done in a row (i.e how long does it take), toguarantee the prediction accuracy of 99%.The solution described in the thesis is based on Condorcet’s jury theorem [1].It states that if we have two options and the chance to pick correct is larger than50%, then, if we make several attempts in a row, the probability to pick the correctoption by majority vote is rising with the number of attempts. In this work weapply the main Condorcet’s principle in a BCI perspective. First we develop asystem that can reach the single concentration attempt’s prediction accuracy tobe more than 50% and then we use multiple concentration attempts in a row toimprove the overall accuracy. We expect that given enough attempts we can reach99% classification accuracy. We compare the empirical results with the theoreticalestimates and discuss them.The BCI technology is a relatively young field. In order to fully integrate itinto our ordinary life, the contribution from scientists and engineers is required forconverting BCI to a reliable system. The following work contributes to reliabilityof BCI systems

Optimal instance selection for improved decision tree

Instance selection plays an important role in improving scalability of data mining algorithms, but it can also be used to improve the quality of the data mining results. In this dissertation we present a new optimization-based approach for instance selection that uses a genetic algorithm (GA) to select a subset of instances to produce a simpler decision tree with acceptable accuracy. The resultant trees are likely to be easier to comprehend and interpret by the decision maker and hence more useful in practice. We present numerical results for several difficult test datasets that indicate that GA-based instance selection can often reduce the size of the decision tree by an order of magnitude while still maintaining good prediction accuracy. The results suggest that GA-based instance selection works best for low entropy datasets. With higher entropy, there will be less benefit from instance selection. A comparison between GA and other heuristic approaches such as Rmhc (Random Mutation Hill Climbing) and simple construction heuristic, indicates that GA is able to obtain a good solution with low computation cost even for some large datasets. One advantage of instance selection is that it is able to increase the average instances associated with the leaves of the decision trees to avoid overfitting, thus instance selection can be used as an effective alternative to prune decision trees. Finally, the analysis on the selected instances reveals that instance selection helps to reduce outliers, reduce missing values, and select the most useful instances for separating classes

Effective techniques for handling incomplete data using decision trees

Decision Trees (DTs) have been recognized as one of the most successful formalisms for knowledge representation and reasoning and are currently applied to a variety of data mining or knowledge discovery applications, particularly for classification problems. There are several efficient methods to learn a DT from data. However, these methods are often limited to the assumption that data are complete. In this thesis, some contributions to the field of machine learning and statistics that solve the problem of extracting DTs for learning and classification tasks from incomplete databases are presented. The methodology underlying the thesis blends together well-established statistical theories with the most advanced techniques for machine learning and automated reasoning with uncertainty. The first contribution is the extensive simulations which study the impact of missing data on predictive accuracy of existing DTs which can cope with missing values, when missing values are in both the training and test sets or when they are in either of the two sets. All simulations are performed under missing completely at random, missing at random and informatively missing mechanisms and for different missing data patterns and proportions. The proposal of a simple, novel, yet effective proposed procedure for training and testing using decision trees in the presence of missing data is the next contribution. Original and simple splitting criteria for attribute selection in tree building are put forward. The proposed technique is evaluated and validated in empirical tests over many real world application domains. In this work, the proposed algorithm maintains (sometimes exceeds) the outstanding accuracy of multiple imputation, especially on datasets containing mixed attributes and purely nominal attributes. Also, the proposed algorithm greatly improves in accuracy for IM data. Another major advantage of this method over multiple imputation is the important saving in computational resources due to it simplicity. The next contribution is the proposal of three versions of simple probabilistic techniques that could be used for classifying incomplete vectors using decision trees based on complete data. The proposed procedure is superficially similar to that of fractional cases but more effective. The experimental results demonstrate that these approaches can achieve comparative quality to sophisticated algorithms like multiple imputation and therefore are applicable to all kinds of datasets. Finally, novel uses of two proposed ensemble procedures for handling incomplete training and test data are proposed and discussed. The algorithms combine the two best approaches either with resampling (REMIMIA) or without resampling (EMIMIA) of the training data before growing the decision trees. Experiments are used to evaluate and validate the success of the proposed ensemble methods with respect to individual missing data techniques in the form of empirical tests. EMIMIA attains the highest overall level of prediction accuracy

Intermediate Decision Trees

Intermediate decision trees are the subtrees of the full (unpruned) decision tree generated in a breadth-first order. An extensive empirical investigation evaluates the classification error of intermediate decision trees and compares their performance to full and pruned trees. Empirical results were generated using C4.5 with 66 databases from the UCI machine learning database repository. Results show that when attempting to minimize the error of the pruned tree produced by C4.5, the best intermediate tree performs significantly better in 46 of the 66 databases. These and other results question the effectiveness of decision tree pruning strategies and suggest further consideration of the full tree and its intermediates. Also, the results reveal specific properties satisfied by databases in which the intermediate full tree performs best. Such relationships improve guidelines for selecting appropriate inductive strategies based on domain properties. 1 Introduction Numerous decision tree ..