CHAPTER 9 – Text Entry by Gaze: Utilizing Eye Tracking

By Päivi Majaranta, Kari-Jouko Räihä. 2007

112 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Natural Language Processing, Human-Computer Interaction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Tzyy-Ping Jung
2. Xiaogang Chen
3. Xiaorong Gao
4. Shangkai Gao
5. Carrie J. Cai
6. Masaki Nakanishi
7. Yu-Te Wang
8. Bo Hong

In Computer Vision and Pattern Recognition

1. Michael Terry
2. Tzyy-Ping Jung
3. Carrie J. Cai
4. Xiaorong Gao
5. Tongshuang Sherry Wu

In Natural Language Processing

1. Michael Terry
2. Tzyy-Ping Jung
3. Carrie J. Cai
4. Tongshuang Sherry Wu

In Human-Computer Interaction

1. Michael Terry
2. Tongshuang Sherry Wu
3. Tzyy-Ping Jung
4. Shangkai Gao
5. Carrie J. Cai
6. Xiaorong Gao

Citations in the corpus

• AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts (2021, background): “This is based on a real industry use case that aims to speed up gaze input by requiring fewer character inputs [4, 46, 56].”
• Filter bank canonical correlation analysis for implementing a high-speed SSVEP-based brain–computer interface (2015, result): “This spelling rate is comparable to the eye tracking – based spelling system, which typically has a spelling rate around 5 – 10 words/min (Majaranta and Räihä 2007).”