# Multimodal Natural Human-Computer Interfaces for Computer-Aided Design: A Review Paper
## 4 Ways to Implement Natural HCI for CAD
### BCI-Based Interaction for CAD  
Brain-computer interfaces (BCIs) provide a novel communication and control channel from the brain to an output device without the involvement of users' peripheral nerves and muscles [99]. Typically, brain activity can be detected using a variety of approaches, such as EEG, magnetoencephalography (MEG), functional magnetic resonance imaging (fMRI), electrocorticography (ECoG), and near-infrared spectroscopy (NIRS) [100]. Among them, the EEG signal is considered as the input of choice in most BCI systems to relate patterns in brain signals to the users' thoughts and intentions [28]. The very first advancements in this respect were made by [PERSON] [101], who for the first time demonstrated that it is possible for users to move through a virtual street just by imagining their feet movements. [PERSON] [102] and [PERSON] [103] also developed some prototypes allowing users to navigate in virtual scenes and manipulate virtual objects just through EEG signals. Furthermore,Trejo [56] and [PERSON] [104] further extended the application of BCI to cursor movement. These studies show that BCI, as a new non-muscular channel, could be used in many applications involving human cognition, such as computer games, vehicle driving, assistive appliances, and neural prostheses [99].  
In CAD systems, BCI could offer a more intuitive and natural pattern of interaction between the user and CAD application. [PERSON] and [PERSON] [57] conducted the first study that investigated the possibility of using BCI for geometry selection. They used the evoked potential component P300-based BCI for selecting different target surfaces of geometrical objects in the CAD systems. Some other important functions for CAD application such as creating models or manipulating models via BCI have been studied by other researchers [9; 28; 58; 59; 105]. [PERSON] and [PERSON] [58] also carried out experiments to distinguish between different primitive shapes based on users' EEG activity, including cube, sphere, cylinder, pyramid and cone shapes. The EEG headset was used to collect brain signals from 14 locations on the scalp and a linear discriminant classifier was trained to discriminate between the five basic primitive objects with an average accuracy of about 44.6%, significantly above the chance level of 20%. [PERSON] [60] conducted similar studies where the user was able to create and modify geometrical models by using EEG signals. As an important task in CAD applications, model manipulation-based BCI was also realized by [9; 59; 60]. In another recent work, [PERSON] [61] used EEG and electromyogram (EMG) signals from facial movement using the Emotiv headset to enable users to carry out certain tasks in the CAD environment of Google SketchUp. Meanwhile, a human-factors study assessing the usability of the EEG/EMG interface was performed with participants from different backgrounds. The results suggested that the BCI-based system could help to lower the learning curve and has high usability as a generalized medium for CAD modeling [61].  
In sum, BCI shows great potential in allowing users in CAD applications to create, modify and manipulate models directly. However, there are still some common limitations on the practical applicability of BCI resulting from the difficulty of brain signal acquisition and the limited accuracy of instruction discrimination. Future, lightweight EEG acquisition equipment and high-precision classification algorithms will become the new frontier for developing BCI-based CAD applications. Additionally, detecting the emotional state and user's satisfaction from EEG signals could be used in CAD systems to correct for errors and to strengthen proper classifications, which will make the system more reliable [106].