Navigating a smart wheelchair with a brain-computer interface interpreting steady-state visual evoked potentials

Review of real brain-controlled wheelchairs

“…There are three papers about BCWs with low-level navigation (41,48,50), one with high-level navigation (58), and two with shared control (30,55). Although the SSVEP BCWs are based on the selection of a visual stimulus, such as the P300-BCW, we found a clear predominance of prototypical low-level navigation and shared-control systems with four or five commands.…”

A Review on Eeg Control Smart Wheel Chair

“…In [6] band pass filter of 1-35Hz is used. In [14] signal measured is linearly combined with a optimal weight vector to cancel out noise. In [17] signals are filtered using a band pass filter between 0.1Hz-35Hz…”

“…In [10] data is filtered using moving average technique and down sampled by a factor of 16 and all the data from 16 channel is concatenated creating a single feature vector for classification algorithm and uses a stepwise linear discriminate analysis. In [13] 1 second of EEG data is extracted after each stimulus onset and then filtered using moving average method and down sample by a factor of 16,here the number of channel selected varies from six to ten depending on the participant so if ten channel were selected the feature vector length will be 265/16 *10 channels since it is sample at 265hz, stepwise linear discriminate analysis is used to classify P300 obtaining a performance higher than 90%.In [14] the power of each stimulus frequency is calculated from the acquired brain signal and used a linear classifier to classify in which the subject is focused on, command are considered only when they exceeds a particular threshold and if more than one power have exceeded the threshold, the frequency with the highest power is classified. In [16] context based menus of command are displayed to the user and the menus flashes randomly and then EEG data from10ms to 500ms is extracted and fed into a support vector machine which outputs new score for each button, when the score exceeds the threshold it is issue as a command, when one or more button crosses the threshold the menu with highest score is issued as a command.…”

“…In [1] a user can switch between automatic control and subject control, in automatic control the intelligent system take cares of obstacle avoidance in subject control the user takes care of navigating the wheel chair. Some take cumulative decision considering both data from the sensor and machine learning algorithm, in [3] classification output and context based filter output are combined, [14] uses SSVEP to select a command and automatic navigation control perform the task of navigating and obstacle avoidance, [5][10] [13][16] uses P300 to select a location based on options presented using familiar environment or target option shown after scene reconstruction using data read from the sensor. Hybrid approach in terms of signal used are also available, [17] uses P300 and SSVEP, presence of P300 is detected using SVM and power ratio of SSVEP after classification are combine to steer the wheel chair, [18] uses P300 and ERD here P300 is used for speed control and ERD is used for turning left and right.…”

An adaptive controller design with a one-step delay for human machine interface systems

“…These direct, intuitive controls make interaction between human and machine much faster and convenient. They are widely used in prostheses [1], orthoses [2], robotic arms [3] and wheel chairs for disabled people [4].…”