Projects:2015s1-35 Brain computer interface control for biomedical applications

Project Instruction

Objectives

The aim of this project is to design a brain computer interface system for stroke patients. The Emotiv EPOC neuroheadset is used to obtain brain signals, while BCI2000 software platform is utilized to design a signal processing software. A robotic hand is designed to support stroke patient’s hand and give feedback. The system should be able to identify users’ intention of moving fingers, and let the robotic hand move as intended.

Motivation and Significance

According to current researches, on average, for every 10 minutes, an Australian will have a stroke. Besides, stroke costs Australian economy 5 billion dollars a year [23]. In this context, designing a brain computer interface system to help stroke patients regain motor control is a great idea, since this may greatly improve their quality of life and also release the burden on economy.

This project is inspired by the previous work of Traeger and Reveruzzi, which is a similar application of brain computer interface system in biomedical usage [1][2], as well as the earlier work of Wolpaw and McFarland [3][4]. Besides, the theory of Neuroplasticity indicates that by imagining limb movements, neuros in brain may create new pathways instead of the impaired ones to reconstruct the connection with motoring muscles, thus regain control of their limbs. However, these researches mentioned above either focus on arm movement, or uses expensive and complicated devices to achieve robotic hand control. Therefore, we intend to design a cost-effective brain computer interface system that can control robotic hand in this project.

With the help of this system, stroke patients can train their brains so as to rebuild the connection between the brain and motor muscles, and in the end regain motor function. Furthermore, the output of this project may help other researches based on BCI systems, and it can be improved to achieve more complex finger movements.

Requirements

The basic goal of this project is to be able to identify the movement of five fingers, while the optional goal is to be able to identify the movement of each finger individually. Besides, the applicator should be able to move continuously as the user imagines to move.

Background

Brain Computer Interface System

A brain-computer interface (BCI) is defined as a communication system that does not depend on the brain’s normal output pathways of peripheral nerves and muscles [5]. Thus, BCI system is the most direct method of communicating between the human brain and a computer or machine. There are three methods of using BCI: invasive method (signal detection device implanted directly into brain), partially invasive method (devices implanted on the surface of brain and inside the skull), and non-invasive method (collecting brain signal by putting electrodes on the scalp) [6]. Invasive and partially invasive methods can provide high quality signals, however, concerning the safety risks of implanting devices in brain, research in invasive methods mainly focus on non-human primates. In comparison, non-invasive methods are widely experimented on human users because it’s far cheaper and safer, as well as more portable.

Electroencephalography

Electroencephalography (EEG) is the recording of electrical activity along the scalp. It is the most studied potential non-invasive interface, mainly because of its fine temporal resolution, ease of use, portability and low set-up cost. The concept of implementing an EEG signal to control a machine existed as early as the 1970s [7], however, it was not until 1999, when the first experimental demonstration was performed, that neuron activity was used to control a robot’s arm [8]. After that, this field has undergone enormous development. EEG research reflects two major types: evoked potentials (transient waveforms that are phase-locked to an event, such as a visual stimulus) and oscillatory features (occur in response to specific events, typically studied through spectral analysis) [9]. Three kinds of EEG-based BCI have been tested in human beings, and they are distinguished by the unique features they use to determine the user’s intent. Among the three, P300 event-related brain potential is mostly stimulated visually, while slow cortical potentials (SCPs) are mainly used for basic word processing and other simple control tasks. The third one is sensorimotor rhythms, which will be used in this project.

Sensorimotor Rhythms

Sensorimotor rhythms (SMR) are well suited for this project, because its amplitudes change with the imagination of movement (also called motor imagery). These rhythms are 8–12 Hz (μ) and 18–26 Hz (β) oscillations in the EEG signals recorded over sensorimotor cortices. Movement or preparation for movement is typically accompanied by a decrease in μ and β activity over sensorimotor cortex [14]. People can learn to control the amplitudes of the two rhythms without any movement or sensation, and use it to move an orthotic device, such as a robotic hand [6][9][10]. It has been proved that the speed and precision of the multidimensional movement control achieved in human beings by SMR method [3][4] equals or exceeds that achieved so far with invasive methods [11][12]. Various BCI designs using SMR method have proved that it is capable of controlling robotic applicators for stroke patients [3][4][13]. Thus, we used it in this project.

Figure 1 shows a typical μ/β rhythm signals. Among these figures, A and B are the topographical distribution on the scalp calculated for actual (A) and imagined (B) right-hand movements and rest for a 3Hz band centred at 12 Hz. C shows an example voltage spectrum for a different subject and a location over left sensorimotor cortex (i.e. C3) for comparing rest (dashed line) and imagery (solid line). D displays the r^2 value for that specific channel [14]. Details will be demonstrated later in this report.

Neuroplasticity

Although research in neuroplasticity is not in the scope of this project, it is still worth introducing, because it is related with the possible future usage of the deliverables of the project. Neuroplasticity refers to changes in neural pathways and synapses due to changes in behaviour, environment, neural processes, thinking, and emotions, etc. According to the research of Byl Nancy [25], stroke patients can regain control of their upper limbs with the training based on the principles of neuroplasticity. Besides, current researches state that BCI system can provide a non-muscular communication pathway between cerebral activity and body actions for people with devastating neuromuscular disorders, such as stroke [9][14][26]. Thus, BCI systems that concentrate on robotic limb/hand control are often designed to support neuroplasticity research.

System Design

Signal Acquisition

Emotiv EPOC neuroheadset is designed for brain signals applications, it can acquire the raw EEG signals by placing its 14 channels and 2 reference channels on the scalp.

In this project, Emotiv EPOC Neuroheadset was used to extract the raw EEG signal of subjects.As the requirement of our project, the team members need to do the offline and online signal processing in the BCI2000. The task (interfacing the Emotiv EPOC headset with BCI2000) have been developed in two parts and corresponding two methods to achieve the final goal.

First method(for offline analysis):The method of connecting the Emotiv headset with BCI 2000 source module for the offline analysis was designed.As shown in the flow chart, firstly, the raw EEG signal from the headset will be sent to the Emotiv SDK. Secondly, the trained EEG signal will be sent to the Emotiv TestBench. Then, the EEG signal can be saved as the brain signal data file with the file format EDF (this is the standard file format of TestBench) in the TestBench. Actually, in the TestBench, there is a tool can launch the EDF to the CSV. Furthermore, brain signal data file with the format (CSV and EDF) will be sent to the File Converter to transform these files’ format to BAT (the file format of the BCI2000).Moreover, Compare the EEG signal with the signal in the TestBench. Finally, realizing the interface between Emotiv headsets with BCI2000.

The flow chart of the online method of acquiring the raw EEG signal to the BCI2000 was developed. In this case, after adjusting the EEG signal quality, the raw brain signal from the headset will be directly extract to the BCI2000 .this can be realized by building the emotiv batch file and set the relative parameter and run the batch file in the BCI2000. Finally, achieving the connection of the Emotiv headset to the BCI2000.

Method conclusion:

The merit of the method 1 (offline): The method 1 for the offline is easier than the method 2 for the online. The key of the success is to find a suitable file converter to transform the file format to the BAT (BCI2000 file format).

Shortcomings: This method is only for the BCI2000 offline analysis.

Advantage of method 2 (online): Suitable for the online and offline analysis.

Disadvantage: In the method 2, build Emotiv batch file is required, it is more difficult than the method 1, because, BCI2000 is really complex and source code is changeable.

Conclusion: These two methods are feasible. Therefore these two methods are applied to the connection of the Emotiv Headset to the BCI2000.

Method(Offline

Method(Online)

Signal Processing

BCI2000 System

All BCI systems can be divided in to four modules: Source module (signal acquisition), Signal processing module, application module, and the operator module (graphic user interface) [15].

Project Management

Work Breakdown

There are three aspects in this project: signal acquisition, signal processing and applicator. All members are working in the whole projects. However, Yanbin sun is mainly focusing on the signal acquisition part (interfacing the Emotiv EPOC Neuroheadset with the BCI2000). Xiaotian Wang is in charge of the part of signal processing. And Sishen is responsible for the hardware part.

work breakdown

References

The aim of this project is to utilize the Emotiv Brain Computer Interface system to develop and control a robotic limb support system for stroke patients. The Emotiv allows non-invasive recording of electrical brain activity (EEG), which will be utilized for controlling an applicator. The Emotiv also includes a software development kit. Students will acquire skills in signal processing, feature extraction and feature classification. Further, the students will gain experience by designing a basic medical device.