| P300 |
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High accuracy rate; can achieve recognition rates up to 99%;
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Since no extra hardware or special training is required beyond wearing EEG sensors, it is easy for anyone with minimal technical knowledge to use it effectively;
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Plenty opportunity for optimization due to its long history within BCI research.
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Slow response times due to the large number of trials needed before accurate responses can be obtained;
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Susceptibility noise interference affecting the quality of data collected from participants brains;
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Low information transfer rates, caused largely by the limited number of channels available to record signals.
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| SSVEP |
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Simplicity of a setup process, not needing anything beyond a traditional PC monitor to create an effective interface, along with the general robustness protocol itself capable of detecting small variations in input;
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Thanks to much faster response speeds than many alternatives, SSVEPS offer higher bandwidth transmission values, meaning larger amounts of data can be sent between machine operators in a short period of time, which is important in real-time applications.
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Inability to account for any sudden changes in inputs under circumstances occurring outside the experimenter’s expectation of a result;
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Lack of flexibility and adaptability, demanding certain predetermined conditions be met to ensure the proper functioning of the device;
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Problem regarding the portability aspect since the calibration process requires considerable effort in order to setup the the system in the first place before actual testing begins while also taking quite a bit power to run continuously and maintain accuracy throughout the duration of the session, causing major hindrances in mobile implementations where size and weight matter most.
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| MI |
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Flexibility, since no physical movement is required; it allows users with disabilities or limited mobility greater freedom in terms of how they interact with computers and other electronic devices.
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Since EEG signals generated by MI are relatively easy to detect compared to other BCI paradigms such as SSVEP or P300, there is less need for complex signal processing algorithms, which makes implementation easier and faster overall;
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Due its non-invasive nature, this method does not require costly hardware setups or any special training, thus keeping setup costs low while increasing accessibility for anyone wanting to use technology.
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Difficulty in distinguishing between actual and imagined motor tasks under the same conditions, leading to significant confusion errors;
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Accuracy rate drops heavily depending on task complexity and the number of decisions needed to be made during a single session, meaning some applications may not be suitable given current limitations despite the potential advantages offered by the protocol itself;
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Due reliance purely on internal processes such as imagination, fatigue becomes a major problem, especially for longer sessions, causing a drop performance over time and the need for additional breaks to recover properly before continuing operation, further reducing the effectiveness of the system as a whole.
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