Cursor movement detection in brain-computer-interface systems using the K-means clustering method and LSVM

The relationship between humans and computer is one of the research fields in which many investments have been made in recent years. Over the past two decades, various human-computer-interfaces have been designed that utilized audition, vision, somatosensation or a combination of them. Serious efforts have been made since the past decade to relate the computer to the environment based on analysis of brain electrical signals. The main purpose of these systems is to help people with corticospinal damages. Despite of having healthy brains, these people are usually not able to move or build a normal and natural relationship with their surrounding environment. Recent advances in neuroscience, physiology, signal analysis, machine learning, and hardware have made it possible to design direct brain-computer communication systems, known as brain-computer-interfaces (BCI), which enable the physically disabled patients to do their affairs without the help of others. In general, a brain-computer interface is a system that allows people with disabilities to operate electrical devices like a computer pointer, a robotic arm, or even a cellular phone with their mind. Regarding its implementation, a BCI system evaluates the specific features of the brain activity, and accordingly translates them into the signals related to device control. The input signals may be electromagnetic [1], infrared spectroscopy [2‐7], magnetic resonance imaging [8, 9], electro-echography [10] and EEG signal [11, 12]. Among these techniques, the EEG signal is used more widely due to its simplicity, low cost, non-invasiveness and ability to register and analyze the data.

A BCI program specifies appropriate physical or mental tasks and selects electrodes related to these tasks. It extracts features of signal recording by means of these electrodes, and ultimately produces an algorithm with the highest classifiers. Then, it the information is transferred to communication and control units through implementing the algorithms to translation. Developing a quick and precise classification method is a key subject for communication and control over the EEG signal. Computer cursor control is one of the most useful applications of BCI. The first studies on the cursor control based on EEG signal were focused on one-dimensional control of cursor [13, 14]. After obtaining satisfactory results, the attention of researchers was drawn to the multi-dimensional cursor control with the aim of increasing the relationship between the user and machine. Based on the studies, the multi-dimensional cursor can be controlled by implementing different brain signals, for example the P300 potential [15, 16], synchronous and asynchronous signals [17] and evoked potentials [18, 19]. They showed well that the input signals of an EEG-based brain-computer-interface system have commonly weak, non-constant and mind tasks-related noises with various artifacts such as external electromagnetic waves and electromyogram and electro-echogram waves. All of these defects were considered by researchers to improve the key features of BCI system function including the classifier velocity and precision. In addition to improving the functional features, the operational and realistic construction of the EEG-based BCI systems was another major challenge from the viewpoint of researchers. It highly relies on using a training process, which is based on a small number of training signals, classifying channel algorithms and features which can represent the studies tasks better. The proper input design, features extraction and appropriate classification of a BCI system has been undertaken over the past 20 years. Various features of the EEG signal serve as inputs for a BCI, such as the Mu band (8–12 HZ) and beta band (18–25 HZ) [20], event-related potential such as P300 [21, 22], steady-state visual-evoked response or the surface slow cortical potential (SPCs) [23, 24], and several feature extraction methods for motor imagery data as input signals, including AR parameter [25] estimation in a specific frequency band [20, 26], domain of slow cortical potential [27], common spatial pattern, event-related synchronization (ERD / ERS), wavelet correlation coefficient [28], and spatial pattern spectrum [29]. Many of the classifier algorithms, such as K-nearest Neighberhood (KNN), Linear Discriminate Analysis (LDA) [30], hidden Markov model [31], Neural Networks [32, 33], Multilayer Perceptron, and Mahalanobis Distance (MDA) [34, 35] were tested for BCI programs. The feature extraction and classifier algorithm has been investigated in BCI applications [36]. Since 2001, several BCI competitions have been organized for improved target assessment and comparison of methods for EEG data derived from mental tasks and also addressing of key issues in BCI research, and significant results have been reported for BCI data. Currently, BCI technology lacks desirable speed and precision, and these are two important variables affecting current attempts to address issues related to the creation of real BCI systems in the future.To this aim, the classifier can discriminate between the EEG signals recorded in different sessions and days with the mental tasks belong to the BCI system. It should also be borne in mind that BCI systems have the potential to help people of all ages with severe mobility impairments. Although some studies on the BCI performance in older people didn’t yield good results due to poor rate of data transfer, .In the BCI competitions, [25] obtained a classifier precision of 88.7 % with a linear classifier using gamma band power (Channel 4) and SCPs (Channel 1) [36]. Then, by selecting a neural network as classifier, the classifier accuracy was enhanced to 91.47 %. In addition, [21, 30] by extracting features of 6 channels and using wavelet transform algorithm, they obtained a classifier accuracy of 90.8 % by the neural network. [30, 37] used principal component analysis (PCA) in 6 channels and their spectral properties, reporting an accuracy of 90.44 %. Trejo et al. [38] showed that people of various ages can benefit from this technology. It has also been observed in recent studies that the combination of EEG and EMG signals in BCI systems can be used for several activities in cases of mobility impairments. They believed that users would be able to understand how to interact with such interactive systems [39]. Although these methods have desirable classifier accuracy, according to complete training data, they were capable of using more than one electrode and complex vectors. Therefore, they are complex, low portable and in need of extensive training and computational time. Their results not only revealed the accuracy of diagnosis, but also significantly reduced the calculation time for training due to the reduced feature space. However, the drawback of these methods is that they used training accuracy as a criterion for the evaluation of different combinations of features. In fact, different subsets of features were trained for creating the optimal subset with the best accuracy for diagnosis, although it was ineffective. The objective of this study is to classify the EEG signal in the BCI systems using data mining technique. As we know, data mining plays a significant role in extraction of hidden information or patterns and relationships in a large amount of data. Feature extraction and selection are very important in data mining. In addition, an increase occurs in the computational time when the number of descriptive features increases. To deal with the large number of features in the present study, a method for recognizing the pattern and extracting the essential information for detection of the cursor movement has been studied. The present study aimed to find a method for the input motion efficently and efficently by implementing the data mining technique. This is done using the hybrid K-means clustering method and the linear support vector machine (LSVM) classifier [40] for detecting the upward, downward, leftward and rightward motion.

This study proposes a simple and straightforward algorithmic method to improve the classification of BCI data. A smaller number of features reduces the computational times. Although the speed comparison with other studies cannot be made objectively since they did not report any results on the training times or the testing times. The curser movements pattern recognition suggests a new way on how to determine features for the the up- and downward and left- and rightward movements. The K-LSVM reduces the input features space dimensions and reconstructs the format of the features to optimize the classifier. So the training time has been reduced and the accuracy is improved. Because the noisy information has been eliminated.

The present paper’s agenda is as follows: Sect. "Materials and methods"  describes the collection of data used for classification. Besides, it presents the K-means algorithm was proposed as an unsupervised algorithm to extract motion features and prevent from repetiting the training in different subsets. All of the data related to feature can be stored in a dense method for one-time training instead of performing multiple training pilots in different feature subsets since the K-means algorithm clusters the principal feature space with unsupervised learning. Therefore, a membership function was used to obtain optimal results from the K-means algorithm for training. Further, Sect. "Results"  provides a summary of a combination of K-means and LSVM classifier for motion detection with high accuracy has been proposed in this study. Lastly, Sect. "Discussion" and "Conclusion" give critical insights into the performed interpretation and accuracy, and address some limitations and possibilities of the presented method.

The results have been obtained from three subjects by determining the optimal channel for each of them. The proposed method is the hybrid K-means clustering and LSVM classifier.

The \(K\)-means clustering has been used for recognizing the hidden patterns and the LSVM classifier for separating the up- downward and the left-rightward movements. The optimal channel for each person has been calculated according to Table 3. In this method, once the up- downward mode and then the left- rightward mode are classified. Based on the results, the LSVM classifier in the proposed method has effective results, as the applied hybrid method has led to an increase of 2–10 % in the accuracy of the classifier.

Sensitivity and specificity are two statistical measures for evaluating the outcome of a binary classification test. When data can be divided into two positive and negative groups, the accuracy of the results of a test that divides the data into these two groups can be measured and described using the indices of sensitivity and specificity. Sensitivity (the true positive rate) is the proportion of positives that are correctly identified as positive by the test. Specificity (the true negative rate) is the proportion of negatives that are correctly identified as negative by the test. Mathematically, sensitivity is expressed as the ratio of true positives to combined true and false positives.

Sensitivity and specificity for up- downward and left- rightward movements in the proposed method are calculated based on the relations (15) and (16), as seen in Table 4.

In this study, K-LSVM method was proposed based on known special patterns. This can be comparable to conventional data mining methods in motion detection. In the feature extraction step, conventional data mining methods are not used; instead, clustering is utilized to extract symbolic motions with the aim of representing the cluster of motions. The relationship between input motion and symbolic motion as the pattern membership is evaluated to predict new motions. The proposed method is comparable to Aydemir et al. [41] with same dataset and features. They classified the EEG signal based on the decision tree structure during the cursor movement. They could separate the up- and downward and left- and rightward movements by achieving the accuracy of 55.92 %, 57.92 and 80.24 % for the first, second and third person, respectively. As the feature applied in their approach is according to Table 2 and the three classifiers LDA, KNN and SVM have been used for separating the movements, the applied decision tree structure is different in each person and this is one of the weak points of this approach, while the hybrid algorithm applied in the proposed method in the present study, considering the use of the same features, is similar for all subjects, and hence, the proposed method has caused an increase of 2–10 % in the classifier accuracy. There are no researches on hybrid clustering and classifiers in BCI systems and recent studies using this approach have been concentrated on breast cancer diagnosis. Bennett  et al. [43] tested this algorithm on a set of breast cancer data for clustering the benign and malignant tumors and suggested the detection of data patterns as one of the applications of clustering algorithm. Since the method used by the researchers did not focus on the manner of determining the quantity of clusters in datasets, the test clusters were randomly divided into two benign and malignant categories. The number of patterns should be determined to evaluate the hidden patterns of breast cancer. There is also a need for comprehensive exploration of the connection between the supervised learning and learning without supervision to improve the breast cancer diagnosis. In order to reduce the dimensions of the dataset, some researchers began to use the technique of clustering algorithms and classifier model in the field of machine learning. Dhillon  et al. [44] applied the hybrid clustering algorithm for a set of similar words to achieve a quick and accurate learning in text classification. They used C-means clustering for educational samples and SVM for Text identification. Radha et al. [45] used a breast cancer database for their researches in 2014. Their clustering was based on motor size. They applied the K-means algorithm to this dataset. Nine features were used in this method. In the first phase of the K-means clustering algorithm, the Manhattan distance criterion was used to measure the distance between nodes and the selection of the center of the primary cluster was done randomly. After implementing the algorithm at k = 2 on 286 data points, the average probability of the correct classification was 66.99 %. In the second phase of K-means clustering, the Euclidean distance criterion was used to measure the distance between nodes and the selection of the center of the primary cluster was done randomly. After implementing the algorithm at k = 2 on 286 data points, the average probability of the correct classification was 74.98 %. In the third phase of K-means clustering, the cluster centers were graded in order to raise the clustering accuracy and the Pearson’s correlation criterion was used to measure the distance between nodes and the selection of the centers of the primary cluster was achieved through co integration. After implementing the algorithm at k = 2 on 286 data points, the same results were obtained from all clustering. The average probability of the correct classification was 100 %. Based on the above-mentioned issues, a large number of methods have been utilized for diagnosing breast cancer based on implementing clustering and classifier. Having a large scale of data is considered as one of the main disadvantages of conventional and traditional methods. Although the time calculations in different training are decreased by applying explorations in feature selection, no comprehensive search is necessary for selecting the feature for the clustering algorithm, especially the K-means algorithm, which results in better understanding of the number of training samples without any help for extracting and selecting the related feature.

The proposed method is comparable to Aydemir et al. [41] with same dataset and features. See Fig. 3. They classified the EEG signal based on the decision tree structure during the cursor movement. The applied decision tree structure is different in each person and this is one of the weak points of this approach, while the hybrid algorithm applied in the proposed method in the present study, considering the use of the same features, is similar for all subjects, and hence, the proposed method has caused an increase of 2–10 % in the classifier accuracy.

Deep Neural Networks (DNN), however, have shown promising results in BCI research. Deep neural networks are still lagging behind in performance due to the unavailability of large training datasets [46‐48]. Main disadvantage of this method was its time consuming length.

We compare our proposed method with other studies as seen as Fig. 4. Lee et al. [49] computed the Common Spatial Pattern (CSP) which is the famous feature extraction method in BCI researches. Kayagil et al. [50] and Huang et al. [51] were the most closes ones to this method because of experimental properties. Aydemir et al. have same dataset and features.

In the present study, a combination of K-means clustering algorithm and classifier were implemented to compress the feature extraction space in order to decrease the computational costs. The proposed K-LSVM method in this study emphasizes on discovering the knowledge framework, searching for more knowledge of motion detection through implementing data mining methods. In addition, descriptive features and data will be collected in the future. However, feature extraction and selection are considered as a challenging issue for researchers, although large data with lost values can cause another issue which should be taken into consideration with respect to computational time.

In the present study, K-means was used as clustering algorithm in order to recognize and obtain motion patterns. These patterns are reconstructed as a new feature for the training phase. Pre-processing steps such as feature extraction and selection can play a role in creating a highly effective feature for the purpose of machine learning algorithms which is used for training the classifier. Regarding K-LSVM results, the calculation time can be significantly decreased without measuring the detection accuracy. However, using the k-means algorithm failed to decrease the size of training samples by filtering similar samples in the data, which can be regarded as a potential method for reducing the size of the training set. Based on the results, the LSVM classifier in the proposed method has effective results, as the applied hybrid method has led to an increase of 2–10 % in the accuracy of the classifier. The dataset used in this study represents a complete dataset with lost values; however, applying this method to a broad set of low-size data opens a venue of research in the future. So far, no general rule has been adopted for evaluating the number of patterns for detecting motion. In the future, a method for evaluating the number of symbolic motions can be developed, which may decrease the calculation time for extracting feature and allows experts to comprehend the motions better based on the patterns derived from the methods implemented for data mining methods.