## 2 Related Works  
There is a great deal of interest of BMI for wheelchair applications. For example, in [20] the proposed BMI for wheelchair is equipped with a mapping system in order to provide the user's current location and next possible destinations. The system allowed users to control the interface using motor imaginary by processing brain signals via an algorithm that combines Regularized Common Spatial Patterns (rCSP) and Neural Networks (NN). [PERSON] et al. [21] employed the eye-tracking and EEG to control the movement of the wheelchair. [PERSON] et al. [22] used a Bayesian shared control strategy based on steady-state visual evoked potential (SSVEP) and BMI for wheelchair navigation; whereas, [PERSON] et al. [23] proposed a BMI based on SSVEP of EEG signals to recognize user's intention for controlling the wheelchair and home appliances by using a bluetooth localization system. In [24] the BMI system based on fuzzy neural networks for brain-actuated control of wheelchair. In contrast, here, we propose a BMI based on a novel shadow boundary detection algorithm. To our best knowledge this is the first study that include a shadow boundary detection module in a BMI for wheelchair. In this regard, the most used shadow detection approaches (i.e., invariant-based detection, color model-based detection, interactive shadow-based detection and feature-based detection) are reported. Invariant-based detection aims at finding an independent or unrelated representation of shadows. By comparing the original image with the shadow-free image, the shaded areas can be determined. For example, [PERSON] et al. [25] developed a method to compare 1D illumination invariant shadow-free image with the original image to locate shadow. Experimental results showed that even though the proposed method was able to locate and remove shadows quite effectively, the main limitation of this method is the necessity of a calibrated camera to obtain 1D illumination invariant shadow-free image. [PERSON] and [PERSON] [26] proposed a Fisher linear discriminant to generate invariant images. However, high quality input images (noiseless and uncompressed) and the information about the direction of light were needed to derive an illumination invariant image. [PERSON] et al. [27] took into account the reflection property of object surface and used the bidirectional reflectance distribution function as illumination invariant feature. Although experimental results showed effectiveness and robustness of the proposed method in both indoor and outdoor environments, the limitation was the use of the background image and foreground mask as reference to detect shadows. Color model-based detection relies on the assumption that some significant properties of shadow can be reached by turning colors of an image into different color spaces. In this context, [PERSON] and [PERSON] [28] detected shadows in CIELAB color space by using the luminance value. They observed that the B-channel showed low values in shadow areas. In [29], [PERSON] et. al compared the performance (ROC curve and Z statistic) from \(11\) major color spaces and summarized the best color space model under different conditions. Experimental results showed that when an image is represented in a specific color space, one channel encodes the difference across reflectance edge only and another channel encodes the difference both in shadow and reflectance edges. Hence, the suitable channel can lead to the most accurate classification. However, the drawback of this approach was that dark areas were misclassified as shadow areas. In order to reduce the shadow misclassification phenomena, [PERSON] et al. [30] used normalized RGB (L2 norm) and a 1D-invariant image to generate shadow masks, while, [PERSON] et al. [31] proposed a color space-YCbCr and topological cutting for shadow detection. Fully automatic shadow detection (without the interference from human) from a single image is still a challenging open issue. To this end, the cooperation between users and system is introduced.