走向预测性沟通:大型语言模型和脑计算机接口的融合
1Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
先进的人工智能和神经技术正在融合,以改善辅助通信. 将大型语言模型 (LLM) 与脑计算机接口 (BCI) 拼写器集成,可以增强残疾人的预测性沟通.
科学领域:
- 神经技术和人工智能 人工智能
- 辅助通信技术 辅助通信技术
- 人与计算机的交互
背景情况:
- 人工智能 (AI) 和神经技术的融合为辅助通信提供了重大机会.
- 非侵入性脑电脑界面 (BCI) 拼写正在发展,越来越关注预测性文本功能.
- 大型语言模型 (LLM) 已经迅速发展,提供了复杂的自然语言处理和生成.
研究的目的:
- 检查非侵入性BCI拼写器与LLM用于预测性通信的整合.
- 审查语言模型的演变及其在BCI拼写程序中的应用.
- 确定挑战和未来方向,以加强BCI介导的沟通.
主要方法:
- 审查语言模型从基于规则的系统到深度学习架构的演变.
- 对现有的BCI拼写实现的调查,包括语言建模.
- 在BCI系统中对LLM集成的试点研究的分析.
主要成果:
- 在BCI拼写器中,LLM增强了预测性写作能力.
- 当前的BCI拼写器在打字速度,准确性和用户适应性方面取得了进步.
- 主要挑战仍然存在,包括实时处理,噪声强度和神经解码集成.
结论:
- 将LLM与BCI技术完全整合,有望提供更直观,更有效的神经技术通信解决方案.
- 预计BCI中介通信的速度和可用性将得到改善.
- 这种融合为临床和非临床用户提供了一条通往先进的自适应通信的道路.
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