Converting thought into action…

Kristin Scudder

Bionic Restoration of Movement

There are many disorders that disrupt the neuromuscular channels of the brain, which results in the brain being unable to communicate with its external environment. Brain-machine interfaces (BMI) and brain-computer interfaces (BCI) provide the brain with a new non-muscular channel through which the brain can send messages and demands to the external environment. These interfaces are communication devises that extract electrical signals from the brain, either invasively or non-invasively, and translate them into output actions to control any number of output devices. Much research has been conducted involving ways of improving non-invasive methods to match the performance of invasive methods. One way of improving non-invasive methods is to improve the performance of the feature extraction method. Current BCIs are implemented mainly using the Fast Fourier Transform (FFT) and Autoregressive method (AR). However, research suggests that the Wavelet Packet Transform (WPT) would work better on the non-stationary EEG signals. The goal of this project is to test these three signal processing methods and determine which most accurately determines the user’s intent.

At present, the FFT and AR methods of feature extraction are most commonly used in BCI implementations. However, both these models are unable to describe signal information in various time windows and frequency bands. Since, EEG signals are non-stationary, a feature extraction method that would be able to describe signal information more dynamically is necessary. Accurate feature extraction is essential to the success of BCI implementations in the future. It is this phase, together with the translation algorithm, which is responsible for accurately distinguishing a user’s intent. By improving the accuracy of feature extraction, researchers will be able to implement their BCIs to do much more complicated tasks. In addition, it will bring the non-invasive BCIs closer to invasive BCIs in terms of capabilities.

Communication devises that extract electrical signals from the brain and convert them into output devises to control any number of applications.

BMI: signal extracted from the brain is sent directly to output device

BCI: signal extracted from the brain is first sent to a computer, where it can run a computer-based application, or be sent to another output device

As shown in the above diagram, the signal acquisition module extracts electrical signals. This module then amplifies and digitizes these signals and sends them over to the signal processing module. In the first part of signal processing, specific signal features, which encode the users’ commands, are extracted from the digital signals. In the second part, these signals are sent to a translation algorithm , where the signal features are translated into desired output actions. Some of the desired movements for motor prosthetics include: movement of a cursor, clicking a button, and specification of complex time-varying trajectories, such as reaching for an object. These device commands are sent to the data output module, which uses them to run an output application or an electronic device. In an open-looped BCI, the output is not accessible to the user. However, in a closed loop BCI, this output is sent back to the subject’s brain. The brain uses this feedback to maintain and improve the accuracy of the system. The final module, operating protocol, specifies all the specific details about how the interface will run. It defines how the system will be turned on and off, whether communication is continuous or not, what feedback will be provided to the user, and much more

Utilize electroencephalography (EEG), which is a measurement of electrical activity produced by the brain that is recorded from electrodes placedon the subject’s scalp.Limitations:

• Low information rate, 20-30 bits per minute

• signals obtained represent only a field of potential rather than specific cellular activity

• Insufficient for controlling artificial limbs

Electrodes are implanted into a region of the brain in order to obtain signals via specific neuron firing patterns

Utilize the VEP to determine the direction of a subject’s gaze, in order to control the movement of a cursor or to select a symbol from and 8x8 grid of symbols. The user concentrations on a location or symbol on the screen. Subgroups will quickly be highlighted and the VEP will spike up if the users symbol is in that subgroup.

Slow voltage changes generated in the cortex of the brain. There is a choice located on the top of the screen and another choice located at the bottom of the screen. The selection process takes four seconds. During the first two seconds, the system measures the users initial voltage level. During the last two seconds, the user selects the choice in the top or bottom of the screen by increasing or decreasing the voltage level.

Refers to the peak of 300 ms reached in the EEG when frequent or significant auditory, visual, or somatosensory stimuli are mixed together with frequent or routine stimuli

Mu rhythm is an oscillation measurement representing 8-12 Hz of EEG activity in the primary sensory or motor cortical areas. The beta rhythm is an oscillation measurement representing 18-26 Hz of EEG activity in the somatosensory area of the brain. Both these ranges represent EEG activity when the brain is not engaged in processing input of producing output. Movement or preparation of movement decreases these rhythms, while relaxation increases these rhythms.

Compare three signal processing methods for feature extraction

•Fast Fourier Transform (FFT)•Autoregressive Model (AR)•Wavelet Packet Transform (WPT)

Accurate feature extraction is extremely important, because it is during the signal processing phase of BCI implementations that the user’s intent is determined. Currently, accurate determination of the user’s intent is a key problem in BCI research. Research shows that the WPT Advantages of WPT:

• Multiple resolutions• Faster response and higher accuracy

Collect and use sample EEG data for mental tasks Use data of subjects completing 10 trials EEG samples recorded for 10 seconds during each

mental task• Subjects performed five mental tasks:

• Baseline task• Math task• Geometric figure rotation task• Mental letter-composing task• Visual counting task

Collect additional EEG sample data for motor imagery tasks

Run each method of feature extraction on sample EEG dataAnalyze how well each method differentiates between mental and motor imagery tasks

• Accuracy should be determined by how well the method predicted the user’s intent